Temperature variability, moisture and biotic interactions drive shrub species abundance along a coastal-inland gradient in arctic Greenland
- Analysis workflow -
Jonathan von Oppen (jonathan.vonoppen@bio.au.dk) et al.
20 november 2020
rm(list = ls())Dependencies
Packages included:
- tidyverse - for convenient code flow, data wrangling and plotting
- rjags & R2jags - to link JAGS and R
- skimr - for data summary
- corrr - to output correlation matrices in dataframe format
- cowplot & patchwork - to combine plot panels
- Rmarkdown - to produce this neat little documentation
# load pacman package from the repository, if you do not already have it
if (!require('pacman')) install.packages('pacman', repos="https://cloud.r-project.org")
pacman::p_load(tidyverse, # set of packages for data manipulation, exploration and visualisation
rjags, # to link JAGS and R
R2jags, # to link JAGS and R
skimr, # for quick dataframe inspection
corrr, # output correlation matrices as data frame
cowplot, # combine plot panels
patchwork, # -"-
rmarkdown) # for R Markdown formattingFunctions used:
- effect size plot function
Colour scheme:
theme_darkgreen <- "#13944D"
theme_purple <- "#8757B3"Overview
This project aims to disentangle drivers of abundance of different shrub species and functional groups in arctic tundra.
Analyses will integrate two levels of variation: plot group \(\times\) taxon level for climatic predictors (derived from downscaled CHELSA data), due to their coarser (90m) resolution, and plot \(\times\) taxon level for plot-level topographic and biotic interaction predictors.
The total dataset comprises 4968 observations for 66 variables (data compilation is documented in scripts/jonathan/nuuk_shrub_drivers_data_compilation.R):
env_cov_bio <- read.csv(file.path("..", "data", "processed", "nuuk_env_cover_plots.csv"), header = T) %>%
# rename variable "twi_90m" to "twi"
rename(twi = twi_90m)Data preparation for JAGS models
a) selection of variables relevant for analysis
including predictors
- Information on site, plot, plot group, sampling location (lat, lon, altitude), sampling year
- downscaled CHELSA predictors, averaged over a 30-year period (*[…]_ts_30*)
- solar radiation index (SRI, following Keating et al. 2007), slope (erosion measure), Terrain Ruggedness Index (TRI, following Riley et al. 1999), Normalised Difference Wetness Index (NDWI, Gao 1996), Tasseled Cap Wetness Index (TCWI, Crist & Ciccone 1984), SAGA Topographic Wetness Index (TWI, see Conrad et al. 2015 and the index documentation)
- cover of other shrub species (only used in species models)
- cover of graminoids
- competition pressure in the community (as summed abundance of taller-growing shrub species within a plot, averaged within plot groups)
- taxon (see below for levels)
and response variable
- cover (as relative no. hits per plot for each species/group)
env_cov_bio_sub <- env_cov_bio %>%
select(site_alt_plotgroup_id, plot, site, site_alt_id, year, long, lat, alt, # plot info / metadata
ends_with("_ts_30"), # CHELSA predictors averaged over 30-year period prior to study year
inclin_down, sri, twi, tri,# environmental data
ends_with("_cover"), compet,# biotic predictors: shrub & cover, competition pressure
taxon, cover) # taxon, cover response
head(env_cov_bio_sub)Let’s check for correlation between the different moisture predictors and terrain ruggedness:
(cor_moist <- env_cov_bio %>%
select(twi, ndwi, tcws, tri)) %>%
correlate(diagonal = 1)
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'\(\Rightarrow\) NDWI and TCWS are highly correlated, but both are largely independent from TWI and TRI. We’ll go with TCWS for now, as it is perhaps the most evidence-based moisture predictor (TWI on the other hand should be least confounded by vegetation, but is also rather theoretical).
Predictors don’t always vary between plots within plotgroups - perhaps due to several falling into the same CHELSA grid cell.
Example: plots within site 1, altitude 20, plot group 1: plot P146 is slightly off and therefore has different climate variables than the other ones
Therefore plotgroup-scale (climatic) predictors will have to be averaged before using in models!
env_cov_bio %>% filter(taxon == "Betula nana" & site_alt_plotgroup_id == "1_20_1") %>%
ggplot(aes(x = long, y = lat)) +
geom_point() +
geom_text(aes(label = plot), hjust = 0.0001) +
xlim(c(-51.78675, -51.7863))
env_cov_bio %>% filter(taxon == "Betula nana" & site_alt_plotgroup_id == "1_20_1") %>%
select(site_alt_plotgroup_id, plot, tempjja_ts_30)Check for correlation between predictors:
predictors_set <- env_cov_bio_sub %>%
select(ends_with("_ts_30"), # CHELSA predictors averaged over 10-year period prior to study year
inclin_down, sri, tri, twi, # environmental data
shrub_cover, graminoid_cover, compet) %>% # biotic predictors
names()
# create basic correlation matrix
(cor_mat <- env_cov_bio_sub %>%
dplyr::select(predictors_set) %>%
correlate(diagonal = 1) %>%
# drop all values < .4 to increase readability
mutate_if(is.numeric, ~ ifelse(abs(.) < .4, NA, .)))Note: Using an external vector in selections is ambiguous.
[34mi[39m Use `all_of(predictors_set)` instead of `predictors_set` to silence this message.
[34mi[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'\(\Rightarrow\) Maximum temperature is highly correlated with summer temperature, minimum temperature with continentality, slope (inclin_down) with solar radiation, and spring precipitation variables with summer precipitation. Let’s exclude them step by step and check the variance inflation factors (VIF) along the way. VIF values > 5 indicate collinearity issues.
# for the whole set of predictors
(vif_predictors_1 <- env_cov_bio_sub %>%
dplyr::select(predictors_set) %>%
usdm::vif()) #%>% View()# => drop tempmax (correlated w/ tempjja)
(vif_predictors_2 <- env_cov_bio_sub %>%
dplyr::select(predictors_set,
-tempmax_ts_30) %>%
usdm::vif()) #%>% View()# => drop tempmin (correlated w/ tempcont)
(vif_predictors_3 <- env_cov_bio_sub %>%
dplyr::select(predictors_set,
-tempmax_ts_30,
-tempmin_ts_30) %>%
usdm::vif()) #%>% View()# => drop precipjfmam (correlated w/ precipjja & tempcont)
(vif_predictors_4 <- env_cov_bio_sub %>%
dplyr::select(predictors_set,
-tempmax_ts_30,
-tempmin_ts_30,
-contains("jfm")) %>%
usdm::vif()) #%>% View()# => drop inclin_down (correlated w/ SRI)
(vif_predictors_5 <- env_cov_bio_sub %>%
dplyr::select(predictors_set,
-tempmax_ts_30,
-tempmin_ts_30,
-inclin_down,
-contains("jfm")) %>%
usdm::vif()) #%>% View()# => drop precipmam (correlated w/ precipjja)
(vif_predictors_6 <- env_cov_bio_sub %>%
dplyr::select(predictors_set,
-tempmax_ts_30,
-tempmin_ts_30,
-inclin_down,
-contains("mam")) %>%
usdm::vif()) #%>% View()All VIF are now < 3 -> OK! We can now exclude the dropped variables to obtain the final dataset. In addition, we rename the climate variables and delete the extension:
env_cov_bio_sub <- env_cov_bio_sub %>%
dplyr::select(-tempmax_ts_30,
-tempmin_ts_30,
-inclin_down,
-contains("mam")) %>%
rename(tempjja = tempjja_ts_30,
tempcont = tempcont_ts_30,
precipjja = precipjja_ts_30)
str(env_cov_bio_sub)'data.frame': 4968 obs. of 28 variables:
$ site_alt_plotgroup_id: Factor w/ 69 levels "1_100_1","1_100_2",..: 4 4 4 4 4 4 4 4 4 4 ...
$ plot : Factor w/ 414 levels "P001","P002",..: 145 145 145 145 145 145 145 145 145 145 ...
$ site : int 1 1 1 1 1 1 1 1 1 1 ...
$ site_alt_id : Factor w/ 23 levels "1_100","1_20",..: 2 2 2 2 2 2 2 2 2 2 ...
$ year : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
$ long : num -51.8 -51.8 -51.8 -51.8 -51.8 ...
$ lat : num 64.2 64.2 64.2 64.2 64.2 ...
$ alt : int 20 20 20 20 20 20 20 20 20 20 ...
$ tempjja : num 9.35 9.35 9.35 9.35 9.35 ...
$ tempcont : num 24.9 24.9 24.9 24.9 24.9 ...
$ precipjja : num 214 214 214 214 214 ...
$ sri : num 0.933 0.933 0.933 0.933 0.933 ...
$ twi : num 6.66 6.66 6.66 6.66 6.66 ...
$ tri : num 0.281 0.281 0.281 0.281 0.281 ...
$ graminoid_cover : num 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 ...
$ shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ BetNan_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ CasTet_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ EmpNig_shrub_cover : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
$ PhyCae_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ RhoGro_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ RhoTom_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ SalArc_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ SalGla_shrub_cover : num 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72 ...
$ VacUli_shrub_cover : num 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 ...
$ compet : int 5 23 5 0 0 23 0 23 5 NA ...
$ taxon : Factor w/ 12 levels "All deciduous",..: 4 5 6 8 9 7 11 10 12 3 ...
$ cover : num 0 0 0.72 0 0 0 0.2 0 0 0.92 ...b) adjustion of data structure
Data is ordered by site/altitude/plotgroup and taxon
env_cov_bio_sub <- env_cov_bio_sub[order(env_cov_bio_sub$site_alt_plotgroup_id, env_cov_bio_sub$taxon),]
As JAGS is only able to handle numeric input, all variables are assigned a numeric identifier:
env_cov_bio_sub$plotgroup.NUM <- as.numeric(factor(env_cov_bio_sub$site_alt_plotgroup_id,
levels = unique(env_cov_bio_sub$site_alt_plotgroup_id)))
env_cov_bio_sub$plot.NUM <- as.numeric(factor(env_cov_bio_sub$plot,
levels = unique(env_cov_bio_sub$plot)))
env_cov_bio_sub$site_alt.NUM <- as.numeric(factor(env_cov_bio_sub$site_alt_id,
levels = unique(env_cov_bio_sub$site_alt_id)))
env_cov_bio_sub$site.NUM <- as.numeric(factor(env_cov_bio_sub$site, levels = unique(env_cov_bio_sub$site)))
env_cov_bio_sub$taxon.NUM <- as.numeric(factor(env_cov_bio_sub$taxon, levels = unique(env_cov_bio_sub$taxon)))Taxa were coded as follows:
data.frame(taxon = levels(env_cov_bio_sub$taxon),
num = unique(env_cov_bio_sub$taxon.NUM))Numeric predictors were scaled and centered:
num_pred <- env_cov_bio_sub %>% select(tempjja,
tempcont,
precipjja,
sri,
twi,
tri,
ends_with("_cover"),
matches("compet"))
for(i in 1:length(num_pred)){
col <- colnames(num_pred[i])
env_cov_bio_sub[paste0(col,"C")] <- as.numeric(scale(num_pred[i], scale = TRUE, center = TRUE))
}To account for the range of the cover response (\(0 \leq cover \leq 1\)), the model needs a mixed structure incorporating a beta distribution (for all continuous values with \(0 < cover < 1\)) and a binomial distribution (for all discrete values of \(cover = \{0, 1\}\)). An additional variable cover_discrete was introduced to separate the dataset into discrete (= 1) and continuous (= 0) cover values:
env_cov_bio_sub$cover_discrete <- ifelse(env_cov_bio_sub$cover == 1 | env_cov_bio_sub$cover == 0, 1, 0)JAGS models
The dataset was then ready to be split up into the species of interest. We create separate data subsets for all/discrete/continuous response variable values for each species:
# split dataframe by taxon
env_cov_bio_sub_spec.tot <- split(env_cov_bio_sub, env_cov_bio_sub$taxon)
# assign taxon name to list elements
# >> for total datasets
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
# extract 3-letter genus name string
assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"^\\w{3}"),
# extract and capitalise 3-letter species name string
str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"\\s\\w{3}"),
"\\s")),
# add extension
".tot"),
# assign to respective list element
env_cov_bio_sub_spec.tot[[taxon_id]])
}
# >> for discrete datasets
env_cov_bio_sub_spec.dis <- list()
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
# filter for discrete response values
env_cov_bio_sub_spec.dis[[taxon_id]] <- filter(env_cov_bio_sub_spec.tot[[taxon_id]], cover_discrete == 1)
# extract 3-letter genus name string
assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"^\\w{3}"),
# extract and capitalise 3-letter species name string
str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"\\s\\w{3}"),
"\\s")),
# add extension
".dis"),
# assign to respective list element
env_cov_bio_sub_spec.dis[[taxon_id]])
}
# >> for continuous datasets
env_cov_bio_sub_spec.cont <- list()
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
# filter for continuous response values
env_cov_bio_sub_spec.cont[[taxon_id]] <- filter(env_cov_bio_sub_spec.tot[[taxon_id]], cover_discrete == 0)
# extract 3-letter genus name string
assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"^\\w{3}"),
# extract and capitalise 3-letter species name string
str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
"\\s\\w{3}"),
"\\s")),
# add extension
".cont"),
# assign to respective list element
env_cov_bio_sub_spec.cont[[taxon_id]])
}
# # save all input data
# # groups
# save(list = c("AllShr.tot",
# "AllEve.tot",
# "AllDec.tot"),
# file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_group.datasets.Rdata"))
#
# # species
# # groups
# save(list = c("BetNan.tot",
# "CasTet.tot",
# "EmpNig.tot",
# "PhyCae.tot",
# "RhoGro.tot",
# "RhoTom.tot",
# "SalArc.tot",
# "SalGla.tot",
# "VacUli.tot"),
# file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_species.datasets.Rdata"))
Groups
> assembling data for model input in lists
JAGS needs data input in list format, so all relevant variables are compiled into lists:
# Compile data into lists
# All shrubs ----
shrub_gradient_jags.AllShr.data <- list(
# plot level predictors
cov.tot = AllShr.tot$cover + 1,
plotgroup.tot = AllShr.tot$plotgroup.NUM,
sri.tot = AllShr.tot$sriC,
tri.tot = AllShr.tot$triC,
twi.tot = AllShr.tot$twiC,
gramin_cov.tot = AllShr.tot$graminoid_coverC,
tempjja.tot.plot = AllShr.tot$tempjjaC,
N_plots = length(unique(AllShr.tot$plot)),
# plot group level predictors
tempjja.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(AllShr.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_graminoid_cover = seq(from = min(AllShr.tot$graminoid_coverC), to = max(AllShr.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(AllShr.tot$sriC), to = max(AllShr.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(AllShr.tot$triC), to = max(AllShr.tot$triC), length.out = 100),
xhat_twi = seq(from = min(AllShr.tot$twiC), to = max(AllShr.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(AllShr.tot$tempjjaC), to = max(AllShr.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(AllShr.tot$precipjjaC), to = max(AllShr.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(AllShr.tot$tempcontC), to = max(AllShr.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(AllShr.tot$twiC,0.05),quantile(AllShr.tot$twiC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.AllShr.data)List of 22
$ cov.tot : num [1:414] 1.52 1.84 1.6 1.72 1.52 1.2 1 1.16 1.64 1 ...
$ plotgroup.tot : num [1:414] 1 1 1 1 1 1 2 2 2 2 ...
$ sri.tot : num [1:414] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.tot : num [1:414] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.tot : num [1:414] 0.144 0.144 0.144 -0.384 -0.384 ...
$ gramin_cov.tot : num [1:414] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ tempjja.tot.plot : num [1:414] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ N_plots : int 414
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -1.33 1.82
$ Nxhat2 : num 2# All evergreen shrubs ----
shrub_gradient_jags.AllEve.data <- list(
# plot level predictors
cov.tot = AllEve.tot$cover + 1,
plotgroup.tot = AllEve.tot$plotgroup.NUM,
sri.tot = AllEve.tot$sriC,
tri.tot = AllEve.tot$triC,
twi.tot = AllEve.tot$twiC,
gramin_cov.tot = AllEve.tot$graminoid_coverC,
tempjja.tot.plot = AllEve.tot$tempjjaC,
N_plots = length(unique(AllEve.tot$plot)),
# plot group level predictors
tempjja.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(AllEve.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_graminoid_cover = seq(from = min(AllEve.tot$graminoid_coverC), to = max(AllEve.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(AllEve.tot$sriC), to = max(AllEve.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(AllEve.tot$triC), to = max(AllEve.tot$triC), length.out = 100),
xhat_twi = seq(from = min(AllEve.tot$twiC), to = max(AllEve.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(AllEve.tot$tempjjaC), to = max(AllEve.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(AllEve.tot$precipjjaC), to = max(AllEve.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(AllEve.tot$tempcontC), to = max(AllEve.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(AllEve.tot$twiC,0.05),quantile(AllEve.tot$twiC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.AllEve.data)List of 22
$ cov.tot : num [1:414] 1.52 1.84 1.6 1.64 1.52 1.16 1 1.16 1.64 1 ...
$ plotgroup.tot : num [1:414] 1 1 1 1 1 1 2 2 2 2 ...
$ sri.tot : num [1:414] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.tot : num [1:414] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.tot : num [1:414] 0.144 0.144 0.144 -0.384 -0.384 ...
$ gramin_cov.tot : num [1:414] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ tempjja.tot.plot : num [1:414] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ N_plots : int 414
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -1.33 1.82
$ Nxhat2 : num 2# All decidious shrubs ----
shrub_gradient_jags.AllDec.data <- list(
# plot level predictors
cov.tot = AllDec.tot$cover + 1,
plotgroup.tot = AllDec.tot$plotgroup.NUM,
sri.tot = AllDec.tot$sriC,
tri.tot = AllDec.tot$triC,
twi.tot = AllDec.tot$twiC,
gramin_cov.tot = AllDec.tot$graminoid_coverC,
tempjja.tot.plot = AllDec.tot$tempjjaC,
N_plots = length(unique(AllDec.tot$plot)),
# plot group level predictors
tempjja.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(AllDec.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_graminoid_cover = seq(from = min(AllDec.tot$graminoid_coverC), to = max(AllDec.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(AllDec.tot$sriC), to = max(AllDec.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(AllDec.tot$triC), to = max(AllDec.tot$triC), length.out = 100),
xhat_twi = seq(from = min(AllDec.tot$twiC), to = max(AllDec.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(AllDec.tot$tempjjaC), to = max(AllDec.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(AllDec.tot$precipjjaC), to = max(AllDec.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(AllDec.tot$tempcontC), to = max(AllDec.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(AllDec.tot$twiC,0.05),quantile(AllDec.tot$twiC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.AllDec.data)List of 22
$ cov.tot : num [1:414] 1 1 1 1.08 1 1.04 1 1 1 1 ...
$ plotgroup.tot : num [1:414] 1 1 1 1 1 1 2 2 2 2 ...
$ sri.tot : num [1:414] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.tot : num [1:414] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.tot : num [1:414] 0.144 0.144 0.144 -0.384 -0.384 ...
$ gramin_cov.tot : num [1:414] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ tempjja.tot.plot : num [1:414] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ N_plots : int 414
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -1.33 1.82
$ Nxhat2 : num 2# # save model input data
# shrub_gradient_jags.groupdata <- list
#
# save(list = c("shrub_gradient_jags.AllShr.data",
# "shrub_gradient_jags.AllEve.data",
# "shrub_gradient_jags.AllDec.data"),
# file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_jags.groupdata.Rdata"))> specifying model
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plot ~ dunif(0,100)
tau.plot <- 1/(sigma.plot * sigma.plot)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
# plot level
for (i in 1:N_plots){
cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
b.gramin_cov * gramin_cov.tot[i] +
b.twi * twi.tot[i] +
b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] + # for interaction
b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] + # for interaction
b.sri * sri.tot[i] +
b.tri * tri.tot[i]
}
# plot group level
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] +
b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.groups.jags"))Specify the parameters to be monitored:
params_groups <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", "b.tempcont.x2",
"b.precipjja.x", "b.precipjja.x2",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phat_graminoid_cover",
"phat_sri", "phat_twi", "phat_tri",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")> run & evaluate model
All shrubs
# run model
model_out.shrub_gradient.AllShr <- jags(shrub_gradient_jags.AllShr.data, # input data
inits = NULL, # JAGS to create initial values
params_groups, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllShr) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllShr <- model_out.shrub_gradient.AllShr$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllShr),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllShr <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list)-4)){
ci_90.AllShr[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllShr[param, 3] <- names(data.frame(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllShr <- coeff.shrub_gradient.AllShr %>%
left_join(ci_90.AllShr, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllShr,
file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllShr.Rdata"))
(effect_size_plot.AllShr <- model_plot_sig_function(coeff.shrub_gradient.AllShr, title_string = "all shrubs", plot_width = 10.5))
- unimodal response to summer temperature, negative response to graminoid cover (sig.)
- slightly positive response to solar radiation (n.s.)
As precipitation and temp. variability quadratic terms didn’t come out significant, re-run without them:
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plot ~ dunif(0,100)
tau.plot <- 1/(sigma.plot * sigma.plot)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
# plot level
for (i in 1:N_plots){
cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
b.gramin_cov * gramin_cov.tot[i] +
b.twi * twi.tot[i] +
b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] + # for interaction
b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] + # for interaction
b.sri * sri.tot[i] +
b.tri * tri.tot[i]
}
# plot group level
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.AllShr2.jags"))
# specify new set of parameters
params_AllShr2 <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phat_graminoid_cover",
"phat_sri", "phat_twi", "phat_tri",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
# run model
model_out.shrub_gradient.AllShr2 <- jags(shrub_gradient_jags.AllShr.data, # input data
inits = NULL, # JAGS to create initial values
params_AllShr2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.AllShr2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllShr2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllShr2 <- model_out.shrub_gradient.AllShr2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllShr2),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllShr2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list)-4)){
ci_90.AllShr2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllShr2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllShr2 <- coeff.shrub_gradient.AllShr2 %>%
left_join(ci_90.AllShr2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllShr2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllShr2.Rdata"))
(effect_size_plot.AllShr2 <- model_plot_sig_function(coeff.shrub_gradient.AllShr2, title_string = "all shrubs", plot_width = 8.5))
- positive response to temperature variability, unimodal response to summer temperature, negative response to graminoid cover (sig.)
- positive response to solar radiation (n.s.)
Evergreen shrubs
# run model
model_out.shrub_gradient.AllEve <- jags(shrub_gradient_jags.AllEve.data, # input data
inits = NULL, # JAGS to create initial values
params_groups, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllEve) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllEve <- model_out.shrub_gradient.AllEve$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllEve),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllEve <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list)-4)){
ci_90.AllEve[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllEve[param, 3] <- names(data.frame(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllEve <- coeff.shrub_gradient.AllEve %>%
left_join(ci_90.AllEve, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllEve, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllEve.Rdata"))
(effect_size_plot.AllEve <- model_plot_sig_function(coeff.shrub_gradient.AllEve, title_string = "evergreen shrubs", plot_width = 10.5))
- unimodal response to temperature, negative response to graminoid cover (sig.)
- slightly positive response to solar radiation (n.s.)
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plot ~ dunif(0,100)
tau.plot <- 1/(sigma.plot * sigma.plot)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
# plot level
for (i in 1:N_plots){
cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
b.gramin_cov * gramin_cov.tot[i] +
b.twi * twi.tot[i] +
b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] + # for interaction
b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] + # for interaction
b.sri * sri.tot[i] +
b.tri * tri.tot[i]
}
# plot group level
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.AllEve2.jags"))
# specify new set of parameters
params_AllEve2 <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phat_graminoid_cover",
"phat_sri", "phat_twi", "phat_tri",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
# run model
model_out.shrub_gradient.AllEve2 <- jags(shrub_gradient_jags.AllEve.data, # input data
inits = NULL, # JAGS to create initial values
params_AllEve2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.AllEve2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllShr2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllEve2 <- model_out.shrub_gradient.AllEve2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllEve),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllEve2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list)-4)){
ci_90.AllEve2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllEve2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllEve2 <- coeff.shrub_gradient.AllEve2 %>%
left_join(ci_90.AllEve2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllEve2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllEve2.Rdata"))
(effect_size_plot.AllEve2 <- model_plot_sig_function(coeff.shrub_gradient.AllEve2, title_string = "evergreen shrubs", plot_width = 8.5))
- unimodal response to temperature, negative response to graminoid cover (sig.)
- slightly positive response to solar radiation (n.s.)
Deciduous shrubs
# run model
model_out.shrub_gradient.AllDec <- jags(shrub_gradient_jags.AllDec.data, # input data
inits = NULL, # JAGS to create initial values
params_groups, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllDec) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllDec <- model_out.shrub_gradient.AllDec$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllDec),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllDec <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list)-4)){
ci_90.AllDec[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllDec[param, 3] <- names(data.frame(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllDec <- coeff.shrub_gradient.AllDec %>%
left_join(ci_90.AllDec, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllDec, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllDec.Rdata"))
(effect_size_plot.AllDec <- model_plot_marg_function(coeff.shrub_gradient.AllDec, title_string = "deciduous shrubs", plot_width = 10.5))
- positive/unimodal response to summer temperature & temp. variability, negative response to graminoid cover (sig.)
- slightly positive response to solar radiation, negative response to terrain heterogeneity (n.s.)
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plot ~ dunif(0,100)
tau.plot <- 1/(sigma.plot * sigma.plot)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
# plot level
for (i in 1:N_plots){
cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
b.gramin_cov * gramin_cov.tot[i] +
b.twi * twi.tot[i] +
b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] + # for interaction
b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] + # for interaction
b.sri * sri.tot[i] +
b.tri * tri.tot[i]
}
# plot group level
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.AllDec2.jags"))
# specify new set of parameters
params_AllDec2 <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phat_graminoid_cover",
"phat_sri", "phat_twi", "phat_tri",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
# run model
model_out.shrub_gradient.AllDec2 <- jags(shrub_gradient_jags.AllDec.data, # input data
inits = NULL, # JAGS to create initial values
params_AllDec2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.AllDec2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.AllDec2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.AllDec2 <- model_out.shrub_gradient.AllDec2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllDec2),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.AllDec2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list)-4)){
ci_90.AllDec2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.AllDec2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.AllDec2 <- coeff.shrub_gradient.AllDec2 %>%
left_join(ci_90.AllDec2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.AllDec2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllDec2.Rdata"))
(effect_size_plot.AllDec2 <- model_plot_marg_function(coeff.shrub_gradient.AllDec2, title_string = "deciduous shrubs", plot_width = 9.5))
- positive/unimodal response to temperature variability, positive response to soil moisture, negative response to graminoid cover (sig.)
- positive response to solar radiation (m.s.)
- slightly positive response to temperature, negative response to terrain heterogeneity (n.s.)
Species
> species: assembling data for model input in lists
# BetNan ----
shrub_gradient_jags.BetNan.data <- list(
# plot level predictors, for discrete...
cov.dis = BetNan.dis$cover,
plotgroup.dis = BetNan.dis$plotgroup.NUM,
tempjja.dis = BetNan.dis$tempjjaC,
sri.dis = BetNan.dis$sriC,
tri.dis = BetNan.dis$triC,
twi.dis = BetNan.dis$twiC,
shrub_cov.dis = BetNan.dis$BetNan_shrub_coverC,
gramin_cov.dis = BetNan.dis$graminoid_coverC,
compet.dis = BetNan.dis$competC,
N_discrete = nrow(BetNan.dis),
# ...and continuous part of the data
cov.cont = BetNan.cont$cover,
plotgroup.cont = BetNan.cont$plotgroup.NUM,
tempjja.cont = BetNan.cont$tempjjaC,
sri.cont = BetNan.cont$sriC,
tri.cont = BetNan.cont$triC,
twi.cont = BetNan.cont$twiC,
shrub_cov.cont = BetNan.cont$BetNan_shrub_coverC,
gramin_cov.cont = BetNan.cont$graminoid_coverC,
compet.cont = BetNan.cont$competC,
N_cont = nrow(BetNan.cont),
# plot group level predictors
tempjja.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(BetNan.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(BetNan.tot$competC), to = max(BetNan.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(BetNan.tot$BetNan_shrub_coverC), to = max(BetNan.tot$BetNan_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(BetNan.tot$graminoid_coverC), to = max(BetNan.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(BetNan.tot$sriC), to = max(BetNan.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(BetNan.tot$triC), to = max(BetNan.tot$triC), length.out = 100),
xhat_twi = seq(from = min(BetNan.tot$twiC), to = max(BetNan.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(BetNan.tot$tempjjaC), to = max(BetNan.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(BetNan.tot$precipjjaC), to = max(BetNan.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(BetNan.tot$tempcontC), to = max(BetNan.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(BetNan.tot$twiC,0.05),quantile(BetNan.tot$twiC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.BetNan.data)List of 36
$ cov.dis : num [1:223] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:223] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:223] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:223] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:223] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:223] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:223] 0.451 1.316 0.668 0.992 0.451 ...
$ gramin_cov.dis : num [1:223] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:223] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 223
$ cov.cont : num [1:191] 0.08 0.04 0.08 0.12 0.08 0.56 0.24 0.2 0.08 0.12 ...
$ plotgroup.cont : num [1:191] 5 5 6 10 10 11 11 11 11 12 ...
$ tempjja.cont : num [1:191] -0.42 -0.42 -0.322 1.009 1.009 ...
$ sri.cont : num [1:191] 0.823 -0.248 -0.663 -0.678 -0.192 ...
$ tri.cont : num [1:191] 0.4613 0.3752 -0.0373 -0.2759 0.1133 ...
$ twi.cont : num [1:191] 1.78 1.78 1.22 -1.29 -1.29 ...
$ shrub_cov.cont : num [1:191] 2.397 1.1 -0.197 -0.629 -0.954 ...
$ gramin_cov.cont : num [1:191] 0.965 -0.582 -0.582 -0.195 -0.195 ...
$ compet.cont : num [1:191] -0.477 -0.708 -0.708 -0.708 -0.708 ...
$ N_cont : int 191
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.671 -0.633 -0.596 -0.559 ...
$ xhat_shrub_cover : num [1:100] -0.954 -0.905 -0.855 -0.806 -0.757 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -1.33 1.82
$ Nxhat2 : num 2# CasTet ----
shrub_gradient_jags.CasTet.data <- list(
# plot level predictors, for discrete...
cov.dis = CasTet.dis$cover,
plotgroup.dis = CasTet.dis$plotgroup.NUM,
tempjja.dis = CasTet.dis$tempjjaC,
sri.dis = CasTet.dis$sriC,
tri.dis = CasTet.dis$triC,
twi.dis = CasTet.dis$twiC,
shrub_cov.dis = CasTet.dis$CasTet_shrub_coverC,
gramin_cov.dis = CasTet.dis$graminoid_coverC,
compet.dis = CasTet.dis$competC,
N_discrete = nrow(CasTet.dis),
# ...and continuous part of the data
cov.cont = CasTet.cont$cover,
plotgroup.cont = CasTet.cont$plotgroup.NUM,
tempjja.cont = CasTet.cont$tempjjaC,
sri.cont = CasTet.cont$sriC,
tri.cont = CasTet.cont$triC,
twi.cont = CasTet.cont$twiC,
shrub_cov.cont = CasTet.cont$CasTet_shrub_coverC,
gramin_cov.cont = CasTet.cont$graminoid_coverC,
compet.cont = CasTet.cont$competC,
N_cont = nrow(CasTet.cont),
# plot group level predictors
tempjja.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(CasTet.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(CasTet.tot$competC), to = max(CasTet.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(CasTet.tot$CasTet_shrub_coverC), to = max(CasTet.tot$CasTet_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(CasTet.tot$graminoid_coverC), to = max(CasTet.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(CasTet.tot$sriC), to = max(CasTet.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(CasTet.tot$triC), to = max(CasTet.tot$triC), length.out = 100),
xhat_twi = seq(from = min(CasTet.tot$twiC), to = max(CasTet.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(CasTet.tot$tempjjaC), to = max(CasTet.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(CasTet.tot$precipjjaC), to = max(CasTet.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(CasTet.tot$tempcontC), to = max(CasTet.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(CasTet.tot$tempjjaC,0.05),quantile(CasTet.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.CasTet.data)List of 36
$ cov.dis : num [1:398] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:398] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:398] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:398] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:398] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:398] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:398] 0.101 0.911 0.304 0.607 0.101 ...
$ gramin_cov.dis : num [1:398] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:398] 0.796 1.722 1.028 1.375 0.796 ...
$ N_discrete : int 398
$ cov.cont : num [1:16] 0.04 0.04 0.08 0.04 0.08 0.72 0.08 0.04 0.16 0.04 ...
$ plotgroup.cont : num [1:16] 30 30 30 38 43 49 49 49 49 49 ...
$ tempjja.cont : num [1:16] 0.262 0.262 0.367 0.569 1.061 ...
$ sri.cont : num [1:16] -0.314 -1.308 -0.87 0.754 -0.746 ...
$ tri.cont : num [1:16] 1.769 1.565 1.324 -0.807 1.567 ...
$ twi.cont : num [1:16] -1.36 -1.36 -1.31 1.9 -1.2 ...
$ shrub_cov.cont : num [1:16] 1.417 0.405 1.518 -0.607 -0.101 ...
$ gramin_cov.cont : num [1:16] -0.195 -0.389 -0.582 -0.582 -0.582 ...
$ compet.cont : num [1:16] 2.3011 1.1437 2.4168 -0.0138 0.5649 ...
$ N_cont : int 16
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.656 -0.603 -0.55 -0.498 ...
$ xhat_shrub_cover : num [1:100] -1.21 -1.17 -1.12 -1.08 -1.03 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# EmpNig ----
shrub_gradient_jags.EmpNig.data <- list(
# plot level predictors, for discrete...
cov.dis = EmpNig.dis$cover,
plotgroup.dis = EmpNig.dis$plotgroup.NUM,
tempjja.dis = EmpNig.dis$tempjjaC,
sri.dis = EmpNig.dis$sriC,
tri.dis = EmpNig.dis$triC,
twi.dis = EmpNig.dis$twiC,
shrub_cov.dis = EmpNig.dis$EmpNig_shrub_coverC,
gramin_cov.dis = EmpNig.dis$graminoid_coverC,
compet.dis = EmpNig.dis$competC,
N_discrete = nrow(EmpNig.dis),
# ...and continuous part of the data
cov.cont = EmpNig.cont$cover,
plotgroup.cont = EmpNig.cont$plotgroup.NUM,
tempjja.cont = EmpNig.cont$tempjjaC,
sri.cont = EmpNig.cont$sriC,
tri.cont = EmpNig.cont$triC,
twi.cont = EmpNig.cont$twiC,
shrub_cov.cont = EmpNig.cont$EmpNig_shrub_coverC,
gramin_cov.cont = EmpNig.cont$graminoid_coverC,
compet.cont = EmpNig.cont$competC,
N_cont = nrow(EmpNig.cont),
# plot group level predictors
tempjja.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(EmpNig.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(EmpNig.tot$competC), to = max(EmpNig.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(EmpNig.tot$EmpNig_shrub_coverC), to = max(EmpNig.tot$EmpNig_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(EmpNig.tot$graminoid_coverC), to = max(EmpNig.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(EmpNig.tot$sriC), to = max(EmpNig.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(EmpNig.tot$triC), to = max(EmpNig.tot$triC), length.out = 100),
xhat_twi = seq(from = min(EmpNig.tot$twiC), to = max(EmpNig.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(EmpNig.tot$tempjjaC), to = max(EmpNig.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(EmpNig.tot$precipjjaC), to = max(EmpNig.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(EmpNig.tot$tempcontC), to = max(EmpNig.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(EmpNig.tot$tempjjaC,0.05),quantile(EmpNig.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.EmpNig.data)List of 36
$ cov.dis : num [1:229] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:229] 2 2 3 5 5 6 6 6 7 7 ...
$ tempjja.dis : num [1:229] -0.283 -0.288 -0.226 -0.42 -0.44 ...
$ sri.dis : num [1:229] 0.9453 -1.4348 -1.4755 0.7837 -0.0244 ...
$ tri.dis : num [1:229] -0.9834 0.1575 0.0119 0.1007 0.1954 ...
$ twi.dis : num [1:229] -0.754 -0.73 -1.069 1.775 2.121 ...
$ shrub_cov.dis : num [1:229] -0.985 -0.985 -0.985 -0.985 -0.985 ...
$ gramin_cov.dis : num [1:229] -0.582 -0.582 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:229] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 229
$ cov.cont : num [1:185] 0.52 0.84 0.6 0.64 0.52 0.16 0.16 0.64 0.16 0.52 ...
$ plotgroup.cont : num [1:185] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.cont : num [1:185] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.cont : num [1:185] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.cont : num [1:185] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.cont : num [1:185] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.cont : num [1:185] -0.985 -0.985 -0.985 -0.745 -0.985 ...
$ gramin_cov.cont : num [1:185] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.cont : num [1:185] -0.708 -0.708 -0.708 -0.477 -0.708 ...
$ N_cont : int 185
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.665 -0.622 -0.578 -0.535 ...
$ xhat_shrub_cover : num [1:100] -0.985 -0.94 -0.895 -0.85 -0.806 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# PhyCae ----
shrub_gradient_jags.PhyCae.data <- list(
# plot level predictors, for discrete...
cov.dis = PhyCae.dis$cover,
plotgroup.dis = PhyCae.dis$plotgroup.NUM,
tempjja.dis = PhyCae.dis$tempjjaC,
sri.dis = PhyCae.dis$sriC,
tri.dis = PhyCae.dis$triC,
twi.dis = PhyCae.dis$twiC,
shrub_cov.dis = PhyCae.dis$PhyCae_shrub_coverC,
gramin_cov.dis = PhyCae.dis$graminoid_coverC,
compet.dis = PhyCae.dis$competC,
N_discrete = nrow(PhyCae.dis),
# ...and continuous part of the data
cov.cont = PhyCae.cont$cover,
plotgroup.cont = PhyCae.cont$plotgroup.NUM,
tempjja.cont = PhyCae.cont$tempjjaC,
sri.cont = PhyCae.cont$sriC,
tri.cont = PhyCae.cont$triC,
twi.cont = PhyCae.cont$twiC,
shrub_cov.cont = PhyCae.cont$PhyCae_shrub_coverC,
gramin_cov.cont = PhyCae.cont$graminoid_coverC,
compet.cont = PhyCae.cont$competC,
N_cont = nrow(PhyCae.cont),
# plot group level predictors
tempjja.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(PhyCae.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(PhyCae.tot$competC), to = max(PhyCae.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(PhyCae.tot$PhyCae_shrub_coverC), to = max(PhyCae.tot$PhyCae_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(PhyCae.tot$graminoid_coverC), to = max(PhyCae.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(PhyCae.tot$sriC), to = max(PhyCae.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(PhyCae.tot$triC), to = max(PhyCae.tot$triC), length.out = 100),
xhat_twi = seq(from = min(PhyCae.tot$twiC), to = max(PhyCae.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(PhyCae.tot$tempjjaC), to = max(PhyCae.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(PhyCae.tot$precipjjaC), to = max(PhyCae.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(PhyCae.tot$tempcontC), to = max(PhyCae.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(PhyCae.tot$tempjjaC,0.05),quantile(PhyCae.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.PhyCae.data)List of 36
$ cov.dis : num [1:404] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:404] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:404] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:404] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:404] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:404] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:404] 0.0921 0.8948 0.2928 0.5938 0.0921 ...
$ gramin_cov.dis : num [1:404] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:404] 0.796 1.722 1.028 1.375 0.796 ...
$ N_discrete : int 404
$ cov.cont : num [1:10] 0.04 0.08 0.08 0.12 0.08 0.04 0.04 0.12 0.44 0.12
$ plotgroup.cont : num [1:10] 9 11 14 17 24 30 36 51 51 69
$ tempjja.cont : num [1:10] 0.0508 0.7561 0.7023 1.0077 1.3121 ...
$ sri.cont : num [1:10] -0.7393 -0.0933 -0.2978 0.4817 0.075 ...
$ tri.cont : num [1:10] -1.127 -0.199 0.634 -0.245 0.119 ...
$ twi.cont : num [1:10] -0.876 -0.637 2.047 -0.85 0.574 ...
$ shrub_cov.cont : num [1:10] -1.21224 2.19909 -0.40957 1.19576 -0.00824 ...
$ gramin_cov.cont : num [1:10] -0.38881 -0.00187 -0.19534 -0.19534 -0.38881 ...
$ compet.cont : num [1:10] -0.708 3.227 0.218 2.07 0.681 ...
$ N_cont : int 10
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.656 -0.603 -0.55 -0.498 ...
$ xhat_shrub_cover : num [1:100] -1.21 -1.17 -1.12 -1.08 -1.03 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# RhoGro ----
shrub_gradient_jags.RhoGro.data <- list(
# plot level predictors, for discrete...
cov.dis = RhoGro.dis$cover,
plotgroup.dis = RhoGro.dis$plotgroup.NUM,
tempjja.dis = RhoGro.dis$tempjjaC,
sri.dis = RhoGro.dis$sriC,
tri.dis = RhoGro.dis$triC,
twi.dis = RhoGro.dis$twiC,
shrub_cov.dis = RhoGro.dis$RhoGro_shrub_coverC,
gramin_cov.dis = RhoGro.dis$graminoid_coverC,
compet.dis = RhoGro.dis$competC,
N_discrete = nrow(RhoGro.dis),
# ...and continuous part of the data
cov.cont = RhoGro.cont$cover,
plotgroup.cont = RhoGro.cont$plotgroup.NUM,
tempjja.cont = RhoGro.cont$tempjjaC,
sri.cont = RhoGro.cont$sriC,
tri.cont = RhoGro.cont$triC,
twi.cont = RhoGro.cont$twiC,
shrub_cov.cont = RhoGro.cont$RhoGro_shrub_coverC,
gramin_cov.cont = RhoGro.cont$graminoid_coverC,
compet.cont = RhoGro.cont$competC,
N_cont = nrow(RhoGro.cont),
# plot group level predictors
tempjja.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(RhoGro.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(RhoGro.tot$competC), to = max(RhoGro.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(RhoGro.tot$RhoGro_shrub_coverC), to = max(RhoGro.tot$RhoGro_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(RhoGro.tot$graminoid_coverC), to = max(RhoGro.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(RhoGro.tot$sriC), to = max(RhoGro.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(RhoGro.tot$triC), to = max(RhoGro.tot$triC), length.out = 100),
xhat_twi = seq(from = min(RhoGro.tot$twiC), to = max(RhoGro.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(RhoGro.tot$tempjjaC), to = max(RhoGro.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(RhoGro.tot$precipjjaC), to = max(RhoGro.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(RhoGro.tot$tempcontC), to = max(RhoGro.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(RhoGro.tot$tempjjaC,0.05),quantile(RhoGro.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.RhoGro.data)List of 36
$ cov.dis : num [1:325] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:325] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:325] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:325] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:325] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:325] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:325] 0.24 1.138 0.464 0.801 0.24 ...
$ gramin_cov.dis : num [1:325] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:325] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 325
$ cov.cont : num [1:89] 0.08 0.12 0.52 0.36 0.28 0.04 0.04 0.16 0.04 0.04 ...
$ plotgroup.cont : num [1:89] 5 11 20 20 20 20 20 20 24 24 ...
$ tempjja.cont : num [1:89] -0.42 0.756 0.374 0.426 0.426 ...
$ sri.cont : num [1:89] 0.8231 -0.9499 -0.0661 -0.0445 -2.5789 ...
$ tri.cont : num [1:89] 0.4613 -0.0943 1.3399 1.4255 0.9241 ...
$ twi.cont : num [1:89] 1.775 -0.637 -1.027 -1.119 -1.119 ...
$ shrub_cov.cont : num [1:89] 2.261 1.026 0.689 0.24 0.689 ...
$ gramin_cov.cont : num [1:89] 0.965 -0.582 -0.582 -0.582 -0.195 ...
$ compet.cont : num [1:89] -0.708 -0.361 -0.708 -0.708 -0.708 ...
$ N_cont : int 89
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.697 -0.685 -0.673 -0.661 ...
$ xhat_shrub_cover : num [1:100] -1.22 -1.17 -1.13 -1.08 -1.04 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# RhoTom ----
shrub_gradient_jags.RhoTom.data <- list(
# plot level predictors, for discrete...
cov.dis = RhoTom.dis$cover,
plotgroup.dis = RhoTom.dis$plotgroup.NUM,
tempjja.dis = RhoTom.dis$tempjjaC,
sri.dis = RhoTom.dis$sriC,
tri.dis = RhoTom.dis$triC,
twi.dis = RhoTom.dis$twiC,
shrub_cov.dis = RhoTom.dis$RhoTom_shrub_coverC,
gramin_cov.dis = RhoTom.dis$graminoid_coverC,
compet.dis = RhoTom.dis$competC,
N_discrete = nrow(RhoTom.dis),
# ...and continuous part of the data
cov.cont = RhoTom.cont$cover,
plotgroup.cont = RhoTom.cont$plotgroup.NUM,
tempjja.cont = RhoTom.cont$tempjjaC,
sri.cont = RhoTom.cont$sriC,
tri.cont = RhoTom.cont$triC,
twi.cont = RhoTom.cont$twiC,
shrub_cov.cont = RhoTom.cont$RhoTom_shrub_coverC,
gramin_cov.cont = RhoTom.cont$graminoid_coverC,
compet.cont = RhoTom.cont$competC,
N_cont = nrow(RhoTom.cont),
# plot group level predictors
tempjja.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(RhoTom.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(RhoTom.tot$competC), to = max(RhoTom.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(RhoTom.tot$RhoTom_shrub_coverC), to = max(RhoTom.tot$RhoTom_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(RhoTom.tot$graminoid_coverC), to = max(RhoTom.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(RhoTom.tot$sriC), to = max(RhoTom.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(RhoTom.tot$triC), to = max(RhoTom.tot$triC), length.out = 100),
xhat_twi = seq(from = min(RhoTom.tot$twiC), to = max(RhoTom.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(RhoTom.tot$tempjjaC), to = max(RhoTom.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(RhoTom.tot$precipjjaC), to = max(RhoTom.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(RhoTom.tot$tempcontC), to = max(RhoTom.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(RhoTom.tot$tempjjaC,0.05),quantile(RhoTom.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.RhoTom.data)List of 36
$ cov.dis : num [1:401] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:401] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:401] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:401] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:401] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:401] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:401] 0.0983 0.9077 0.3006 0.6042 0.0983 ...
$ gramin_cov.dis : num [1:401] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:401] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 401
$ cov.cont : num [1:13] 0.12 0.2 0.12 0.4 0.2 0.2 0.2 0.24 0.04 0.2 ...
$ plotgroup.cont : num [1:13] 11 11 12 30 30 36 39 39 42 43 ...
$ tempjja.cont : num [1:13] 0.756 0.756 0.542 0.148 0.262 ...
$ sri.cont : num [1:13] -0.95 0.723 -0.185 0.633 -1.308 ...
$ tri.cont : num [1:13] -0.0943 -0.2387 -0.4298 1.729 1.5646 ...
$ twi.cont : num [1:13] -0.637 -0.637 0.549 -1.385 -1.362 ...
$ shrub_cov.cont : num [1:13] 0.80655 0.70536 1.21129 0.30062 -0.00293 ...
$ gramin_cov.cont : num [1:13] -0.582 -0.582 1.546 -0.582 -0.389 ...
$ compet.cont : num [1:13] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_cont : int 13
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ xhat_shrub_cover : num [1:100] -1.22 -1.17 -1.13 -1.08 -1.03 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# SalArc ----
shrub_gradient_jags.SalArc.data <- list(
# plot level predictors, for discrete...
cov.dis = SalArc.dis$cover,
plotgroup.dis = SalArc.dis$plotgroup.NUM,
tempjja.dis = SalArc.dis$tempjjaC,
sri.dis = SalArc.dis$sriC,
tri.dis = SalArc.dis$triC,
twi.dis = SalArc.dis$twiC,
shrub_cov.dis = SalArc.dis$SalArc_shrub_coverC,
gramin_cov.dis = SalArc.dis$graminoid_coverC,
compet.dis = SalArc.dis$competC,
N_discrete = nrow(SalArc.dis),
# ...and continuous part of the data
cov.cont = SalArc.cont$cover,
plotgroup.cont = SalArc.cont$plotgroup.NUM,
tempjja.cont = SalArc.cont$tempjjaC,
sri.cont = SalArc.cont$sriC,
tri.cont = SalArc.cont$triC,
twi.cont = SalArc.cont$twiC,
shrub_cov.cont = SalArc.cont$SalArc_shrub_coverC,
gramin_cov.cont = SalArc.cont$graminoid_coverC,
compet.cont = SalArc.cont$competC,
N_cont = nrow(SalArc.cont),
# plot group level predictors
tempjja.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(SalArc.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(SalArc.tot$competC), to = max(SalArc.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(SalArc.tot$SalArc_shrub_coverC), to = max(SalArc.tot$SalArc_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(SalArc.tot$graminoid_coverC), to = max(SalArc.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(SalArc.tot$sriC), to = max(SalArc.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(SalArc.tot$triC), to = max(SalArc.tot$triC), length.out = 100),
xhat_twi = seq(from = min(SalArc.tot$twiC), to = max(SalArc.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(SalArc.tot$tempjjaC), to = max(SalArc.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(SalArc.tot$precipjjaC), to = max(SalArc.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(SalArc.tot$tempcontC), to = max(SalArc.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(SalArc.tot$tempjjaC,0.05),quantile(SalArc.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.SalArc.data)List of 36
$ cov.dis : num [1:401] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:401] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:401] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:401] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:401] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:401] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:401] 0.09 0.894 0.291 0.592 0.09 ...
$ gramin_cov.dis : num [1:401] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:401] 0.796 1.722 1.028 1.375 0.796 ...
$ N_discrete : int 401
$ cov.cont : num [1:13] 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.2 0.08 0.04 ...
$ plotgroup.cont : num [1:13] 4 4 5 9 16 17 18 30 43 45 ...
$ tempjja.cont : num [1:13] -0.3953 -0.3953 -0.4199 0.0508 1.3027 ...
$ sri.cont : num [1:13] -1.344 -2.825 0.823 0.14 0.305 ...
$ tri.cont : num [1:13] 1.498 1.327 0.461 -0.478 -0.95 ...
$ twi.cont : num [1:13] 1.132 1.132 1.775 -0.876 -1.18 ...
$ shrub_cov.cont : num [1:13] 0.291 0.09 1.898 -0.111 -0.714 ...
$ gramin_cov.cont : num [1:13] -0.582 -0.582 0.965 0.772 -0.582 ...
$ compet.cont : num [1:13] 1.028 0.796 2.88 0.565 -0.13 ...
$ N_cont : int 13
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.656 -0.603 -0.55 -0.498 ...
$ xhat_shrub_cover : num [1:100] -1.22 -1.17 -1.12 -1.08 -1.03 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# SalGla ----
shrub_gradient_jags.SalGla.data <- list(
# plot level predictors, for discrete...
cov.dis = SalGla.dis$cover,
plotgroup.dis = SalGla.dis$plotgroup.NUM,
tempjja.dis = SalGla.dis$tempjjaC,
sri.dis = SalGla.dis$sriC,
tri.dis = SalGla.dis$triC,
twi.dis = SalGla.dis$twiC,
shrub_cov.dis = SalGla.dis$SalGla_shrub_coverC,
gramin_cov.dis = SalGla.dis$graminoid_coverC,
compet.dis = SalGla.dis$competC,
N_discrete = nrow(SalGla.dis),
# ...and continuous part of the data
cov.cont = SalGla.cont$cover,
plotgroup.cont = SalGla.cont$plotgroup.NUM,
tempjja.cont = SalGla.cont$tempjjaC,
sri.cont = SalGla.cont$sriC,
tri.cont = SalGla.cont$triC,
twi.cont = SalGla.cont$twiC,
shrub_cov.cont = SalGla.cont$SalGla_shrub_coverC,
gramin_cov.cont = SalGla.cont$graminoid_coverC,
compet.cont = SalGla.cont$competC,
N_cont = nrow(SalGla.cont),
# plot group level predictors
tempjja.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(SalGla.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(SalGla.tot$competC), to = max(SalGla.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(SalGla.tot$SalGla_shrub_coverC), to = max(SalGla.tot$SalGla_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(SalGla.tot$graminoid_coverC), to = max(SalGla.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(SalGla.tot$sriC), to = max(SalGla.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(SalGla.tot$triC), to = max(SalGla.tot$triC), length.out = 100),
xhat_twi = seq(from = min(SalGla.tot$twiC), to = max(SalGla.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(SalGla.tot$tempjjaC), to = max(SalGla.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(SalGla.tot$precipjjaC), to = max(SalGla.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(SalGla.tot$tempcontC), to = max(SalGla.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(SalGla.tot$tempjjaC,0.05),quantile(SalGla.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.SalGla.data)List of 36
$ cov.dis : num [1:289] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:289] 1 1 1 1 1 1 2 2 2 2 ...
$ tempjja.dis : num [1:289] -0.321 -0.321 -0.321 -0.345 -0.345 ...
$ sri.dis : num [1:289] 0.51 0.829 1.034 0.37 0.573 ...
$ tri.dis : num [1:289] -0.274 -0.318 -0.301 -0.274 -0.176 ...
$ twi.dis : num [1:289] 0.144 0.144 0.144 -0.384 -0.384 ...
$ shrub_cov.dis : num [1:289] 0.291 1.163 0.509 0.836 0.291 ...
$ gramin_cov.dis : num [1:289] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:289] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 289
$ cov.cont : num [1:125] 0.32 0.12 0.2 0.08 0.44 0.08 0.04 0.12 0.04 0.04 ...
$ plotgroup.cont : num [1:125] 2 2 4 4 6 8 11 11 12 14 ...
$ tempjja.cont : num [1:125] -0.288 -0.288 -0.395 -0.395 -0.4 ...
$ sri.cont : num [1:125] -1.562 -0.758 0.646 -2.68 -0.409 ...
$ tri.cont : num [1:125] -0.0114 0.1046 -0.9626 1.4107 0.1025 ...
$ twi.cont : num [1:125] -0.73 -0.73 1.13 1.13 1.96 ...
$ shrub_cov.cont : num [1:125] -0.69 0.291 0.836 0.727 0.4 ...
$ gramin_cov.cont : num [1:125] -0.582 -0.582 -0.195 -0.582 -0.582 ...
$ compet.cont : num [1:125] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_cont : int 125
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.686 -0.664 -0.642 -0.619 ...
$ xhat_shrub_cover : num [1:100] -1.126 -1.083 -1.04 -0.997 -0.954 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# VacUli ----
shrub_gradient_jags.VacUli.data <- list(
# plot level predictors, for discrete...
cov.dis = VacUli.dis$cover,
plotgroup.dis = VacUli.dis$plotgroup.NUM,
tempjja.dis = VacUli.dis$tempjjaC,
sri.dis = VacUli.dis$sriC,
tri.dis = VacUli.dis$triC,
twi.dis = VacUli.dis$twiC,
shrub_cov.dis = VacUli.dis$VacUli_shrub_coverC,
gramin_cov.dis = VacUli.dis$graminoid_coverC,
compet.dis = VacUli.dis$competC,
N_discrete = nrow(VacUli.dis),
# ...and continuous part of the data
cov.cont = VacUli.cont$cover,
plotgroup.cont = VacUli.cont$plotgroup.NUM,
tempjja.cont = VacUli.cont$tempjjaC,
sri.cont = VacUli.cont$sriC,
tri.cont = VacUli.cont$triC,
twi.cont = VacUli.cont$twiC,
shrub_cov.cont = VacUli.cont$VacUli_shrub_coverC,
gramin_cov.cont = VacUli.cont$graminoid_coverC,
compet.cont = VacUli.cont$competC,
N_cont = nrow(VacUli.cont),
# plot group level predictors
tempjja.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
tempcont.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
precipjja.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
N_plotgroups = length(unique(VacUli.tot$site_alt_plotgroup_id)),
# subset of values for prediction, for each predictor...
xhat_compet = seq(from = min(VacUli.tot$competC), to = max(VacUli.tot$competC), length.out = 100),
xhat_shrub_cover = seq(from = min(VacUli.tot$VacUli_shrub_coverC), to = max(VacUli.tot$VacUli_shrub_coverC), length.out = 100),
xhat_graminoid_cover = seq(from = min(VacUli.tot$graminoid_coverC), to = max(VacUli.tot$graminoid_coverC), length.out = 100),
xhat_sri = seq(from = min(VacUli.tot$sriC), to = max(VacUli.tot$sriC), length.out = 100),
xhat_tri = seq(from = min(VacUli.tot$triC), to = max(VacUli.tot$triC), length.out = 100),
xhat_twi = seq(from = min(VacUli.tot$twiC), to = max(VacUli.tot$twiC), length.out = 100),
xhat_tempjja = seq(from = min(VacUli.tot$tempjjaC), to = max(VacUli.tot$tempjjaC), length.out = 100),
xhat_precipjja = seq(from = min(VacUli.tot$precipjjaC), to = max(VacUli.tot$precipjjaC), length.out = 100),
xhat_tempcont = seq(from = min(VacUli.tot$tempcontC), to = max(VacUli.tot$tempcontC), length.out = 100),
Nxhat = 100,
# ... and for predicting at high/low temperature levels
xhat_twi2 = as.numeric(c(quantile(VacUli.tot$tempjjaC,0.05),quantile(VacUli.tot$tempjjaC,0.95))),
Nxhat2 = 2
)`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)str(shrub_gradient_jags.VacUli.data)List of 36
$ cov.dis : num [1:309] 0 0 0 0 0 0 0 0 0 0 ...
$ plotgroup.dis : num [1:309] 1 1 1 1 2 2 2 2 2 2 ...
$ tempjja.dis : num [1:309] -0.321 -0.321 -0.321 -0.345 -0.283 ...
$ sri.dis : num [1:309] 0.51 0.829 1.034 0.573 0.945 ...
$ tri.dis : num [1:309] -0.274 -0.318 -0.301 -0.176 -0.983 ...
$ twi.dis : num [1:309] 0.144 0.144 0.144 -0.384 -0.754 ...
$ shrub_cov.dis : num [1:309] 0.243 1.121 0.462 0.243 -1.185 ...
$ gramin_cov.dis : num [1:309] -0.582 -0.389 -0.582 -0.582 -0.582 ...
$ compet.dis : num [1:309] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_discrete : int 309
$ cov.cont : num [1:105] 0.08 0.04 0.24 0.28 0.08 0.32 0.36 0.28 0.08 0.16 ...
$ plotgroup.cont : num [1:105] 1 1 3 3 4 4 5 10 10 11 ...
$ tempjja.cont : num [1:105] -0.345 -0.345 -0.226 -0.226 -0.395 ...
$ sri.cont : num [1:105] 0.37 0.688 -0.207 -1.955 -0.556 ...
$ tri.cont : num [1:105] -0.274 0.187 0.138 0.355 1.316 ...
$ twi.cont : num [1:105] -0.384 -0.384 -1.069 -1.069 1.132 ...
$ shrub_cov.cont : num [1:105] 0.572 -0.7454 0.1329 0.6818 0.0231 ...
$ gramin_cov.cont : num [1:105] -0.582 -0.582 -0.582 -0.582 -0.582 ...
$ compet.cont : num [1:105] -0.708 -0.708 -0.708 -0.708 -0.708 ...
$ N_cont : int 105
$ tempjja.tot : num [1:69] -0.333 -0.286 -0.222 -0.396 -0.426 ...
$ tempcont.tot : num [1:69] -2.45 -2.52 -2.49 -2.41 -2.39 ...
$ precipjja.tot : num [1:69] 0.711 0.869 1.058 0.629 0.59 ...
$ N_plotgroups : int 69
$ xhat_compet : num [1:100] -0.708 -0.667 -0.626 -0.585 -0.545 ...
$ xhat_shrub_cover : num [1:100] -1.185 -1.135 -1.085 -1.035 -0.985 ...
$ xhat_graminoid_cover: num [1:100] -0.582 -0.504 -0.426 -0.348 -0.27 ...
$ xhat_sri : num [1:100] -3.11 -3.06 -3.02 -2.98 -2.94 ...
$ xhat_tri : num [1:100] -1.71 -1.65 -1.59 -1.53 -1.47 ...
$ xhat_twi : num [1:100] -1.7 -1.66 -1.62 -1.57 -1.53 ...
$ xhat_tempjja : num [1:100] -2.57 -2.52 -2.48 -2.44 -2.4 ...
$ xhat_precipjja : num [1:100] -1.66 -1.62 -1.58 -1.55 -1.51 ...
$ xhat_tempcont : num [1:100] -2.52 -2.49 -2.45 -2.42 -2.38 ...
$ Nxhat : num 100
$ xhat_twi2 : num [1:2] -2.01 1.31
$ Nxhat2 : num 2# # save model input data
# save(list = c("shrub_gradient_jags.BetNan.data",
# "shrub_gradient_jags.CasTet.data",
# "shrub_gradient_jags.EmpNig.data",
# "shrub_gradient_jags.PhyCae.data",
# "shrub_gradient_jags.RhoGro.data",
# "shrub_gradient_jags.RhoTom.data",
# "shrub_gradient_jags.SalArc.data",
# "shrub_gradient_jags.SalGla.data",
# "shrub_gradient_jags.VacUli.data"),
# file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_jags.speciesdata.Rdata"))> specifying model
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] +
b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.spec.jags"))Specify the parameters to be monitored:
params <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", "b.tempcont.x2",
"b.precipjja.x", "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet", "phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_twi", "phat_tri",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")> run & evaluate model
Betula nana
# run model
model_out.shrub_gradient.BetNan <- jags(shrub_gradient_jags.BetNan.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.BetNan) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.BetNan <- model_out.shrub_gradient.BetNan$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.BetNan),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.BetNan <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list)-4)){
ci_90.BetNan[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.BetNan[param, 3] <- names(data.frame(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.BetNan <- coeff.shrub_gradient.BetNan %>%
left_join(ci_90.BetNan, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat)
save(coeff.shrub_gradient.BetNan, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_BetNan.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_BetNan.Rdata"))
print(coeff.shrub_gradient.BetNan)(effect_size_plot.BetNan <- model_plot_marg_function(coeff.shrub_gradient.BetNan, title_string = "Betula nana", plot_width = 12.5))
- negative response to graminoid cover (sig.)
- unimodal response to temp. variability & other shrub cover (m.s.)
As the quadratic terms for summer temperature and precipitation were not significant, they were removed from the model before re-running:
# new model object with terms removed
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
# b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
# b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
# b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
# b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
# b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
# b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
# b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.BetNan2.jags"))
# specify new set of parameters to be monitored
params_BetNan2 <- c("intercept",
"b.tempjja.x", # "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet", "phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_tri", "phat_twi",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
model_out.shrub_gradient.BetNan2 <- jags(shrub_gradient_jags.BetNan.data, # input data
inits = NULL, # JAGS to create initial values
params_BetNan2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.BetNan2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.BetNan2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.BetNan2 <- model_out.shrub_gradient.BetNan2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# add 90% CIs
ci_90.BetNan2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list)-4)){
ci_90.BetNan2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.BetNan2[param, 3] <- names(data.frame(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.BetNan2 <- coeff.shrub_gradient.BetNan2 %>%
left_join(ci_90.BetNan2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.BetNan2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_BetNan2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_BetNan2.Rdata"))
# effect size plot
(effect_size_plot.BetNan2 <- model_plot_marg_function(coeff.shrub_gradient.BetNan2, title_string = "Betula nana", plot_width = 9.5))
- positive response to temperature variability, negative response to graminoid cover (sig.); negative response to other shrub cover (m.s.)
Cassiope tetragona
model_out.shrub_gradient.CasTet <- jags(shrub_gradient_jags.CasTet.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.CasTet) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.CasTet <- model_out.shrub_gradient.CasTet$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.CasTet),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.CasTet <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list)-4)){
ci_90.CasTet[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.CasTet[param, 3] <- names(data.frame(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.CasTet <- coeff.shrub_gradient.CasTet %>%
left_join(ci_90.CasTet, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat)
print(coeff.shrub_gradient.CasTet)
save(coeff.shrub_gradient.CasTet, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_CasTet.Rdata"))
- not converging: really large R-hat values (~ 6.8) (only 16 non-zero values across plot groups)
Empetrum nigrum
model_out.shrub_gradient.EmpNig <- jags(shrub_gradient_jags.EmpNig.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.EmpNig) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.EmpNig <- model_out.shrub_gradient.EmpNig$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.EmpNig),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.EmpNig <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list)-4)){
ci_90.EmpNig[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.EmpNig[param, 3] <- names(data.frame(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.EmpNig <- coeff.shrub_gradient.EmpNig %>%
left_join(ci_90.EmpNig, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
# save(coeff.shrub_gradient.EmpNig, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_EmpNig.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_EmpNig.Rdata"))
# effect size plot
(effect_size_plot.EmpNig <- model_plot_sig_function(coeff.shrub_gradient.EmpNig, title_string = "Empetrum nigrum", plot_width = 12.5))
- positive response to other shrub cover, precipitation (linear), negative response to competition (sig.)
- slightly negative response to graminoid cover, slightly positive relationship with moisture (n.s.)
As none of the quadratic terms for climatic variables were significant, they were removed from the model before re-running:
# new model object with terms removed
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
# b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
# b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
# b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
# b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
# b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
# b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
# b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.EmpNig2.jags"))
# specify new set of parameters to be monitored
params_EmpNig2 <- c("intercept",
"b.tempjja.x", # "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet", "phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_tri", "phat_twi",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
model_out.shrub_gradient.EmpNig2 <- jags(shrub_gradient_jags.EmpNig.data, # input data
inits = NULL, # JAGS to create initial values
params_EmpNig2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.EmpNig2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.EmpNig2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.EmpNig2 <- model_out.shrub_gradient.EmpNig2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# add 90% CIs
ci_90.EmpNig2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list)-4)){
ci_90.EmpNig2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.EmpNig2[param, 3] <- names(data.frame(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.EmpNig2 <- coeff.shrub_gradient.EmpNig2 %>%
left_join(ci_90.EmpNig2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.EmpNig2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_EmpNig2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_EmpNig2.Rdata"))
# effect size plot
(effect_size_plot.EmpNig2 <- model_plot_sig_function(coeff.shrub_gradient.EmpNig2, title_string = "Empetrum nigrum", plot_width = 9.5))
- positive response to other shrub cover, precipitation (linear), negative response to competition & temperature variability (sig.)
- negative response to graminoid cover, positive relationship with moisture (n.s.)
Phyllodoce caerulea
model_out.shrub_gradient.PhyCae <- jags(shrub_gradient_jags.PhyCae.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.PhyCae) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.PhyCae <- model_out.shrub_gradient.PhyCae$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.PhyCae),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.PhyCae <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list)-4)){
ci_90.PhyCae[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.PhyCae[param, 3] <- names(data.frame(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.PhyCae <- coeff.shrub_gradient.PhyCae %>%
left_join(ci_90.PhyCae, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.PhyCae, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_PhyCae.Rdata"))
- not converged large R-hat values (~ 1.36) (only 10 non-zero values across plot groups)
Rhododendron groenlandicum
model_out.shrub_gradient.RhoGro <- jags(shrub_gradient_jags.RhoGro.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.RhoGro) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.RhoGro <- model_out.shrub_gradient.RhoGro$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoGro),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.RhoGro <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list)-4)){
ci_90.RhoGro[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.RhoGro[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.RhoGro <- coeff.shrub_gradient.RhoGro %>%
left_join(ci_90.RhoGro, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.RhoGro, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_RhoGro.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_RhoGro.Rdata"))
# effect size plot
(effect_size_plot.RhoGro <- model_plot_marg_function(coeff.shrub_gradient.RhoGro, title_string = "Rhododendron groenlandicum", plot_width = 12.5))
- positive response to other shrub cover, negative relationship with precipitation, unimodal response to temperature (sig.)
- negative response to graminoid cover (m.s.)
- slightly positive response to solar radiation, temperature variability (n.s.)
- large variation in competiton response as there was only one (however rare) taller-growing species in the system (R. tomentosum)
As the quadratic terms for temperature variability and precipitation were not significant, they were removed from the model before re-running:
# new model object with terms removed
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.RhoGro2.jags"))
# specify new set of parameters to be monitored
params_RhoGro2 <- c("intercept",
"b.tempjja.x", "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet",
"phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_tri", "phat_twi",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
model_out.shrub_gradient.RhoGro2 <- jags(shrub_gradient_jags.RhoGro.data, # input data
inits = NULL, # JAGS to create initial values
params_RhoGro2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.RhoGro2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.RhoGro2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.RhoGro2 <- model_out.shrub_gradient.RhoGro2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoGro),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.RhoGro2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list)-4)){
ci_90.RhoGro2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.RhoGro2[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.RhoGro2 <- coeff.shrub_gradient.RhoGro2 %>%
left_join(ci_90.RhoGro2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.RhoGro2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoGro2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoGro2.Rdata"))
# effect size plot
(effect_size_plot.RhoGro2 <- model_plot_marg_function(coeff.shrub_gradient.RhoGro2, title_string = "Rhododendron groenlandicum", plot_width = 10.5))
- positive response to temperature variability & other shrub cover, unimodal response to temperature and negative response to precipitation (linear) (sig)
- negative relationship with graminoid cover (n.s.)
- positive response to solar radiation (n.s.)
- large variation in competiton response as there was only one (however rare) taller-growing species in the system (R. tomentosum)
Rhododendron tomentosum
model_out.shrub_gradient.RhoTom <- jags(shrub_gradient_jags.RhoTom.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.RhoTom) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.RhoTom <- model_out.shrub_gradient.RhoTom$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoTom),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.RhoTom <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list)-4)){
ci_90.RhoTom[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.RhoTom[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.RhoTom <- coeff.shrub_gradient.RhoTom %>%
left_join(ci_90.RhoTom, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.RhoTom, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoTom.Rdata"))
- not converging: large R-hat values (~ 1.38), large variation in parameter estimates (only 13 non-zero values)
Salix arctophila
model_out.shrub_gradient.SalArc <- jags(shrub_gradient_jags.SalArc.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.SalArc) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.SalArc <- model_out.shrub_gradient.SalArc$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.SalArc),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.SalArc <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list)-4)){
ci_90.SalArc[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.SalArc[param, 3] <- names(data.frame(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.SalArc <- coeff.shrub_gradient.SalArc %>%
left_join(ci_90.SalArc, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.SalArc, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalArc.Rdata"))
- not converging well: relatively large R-hat values (~ 1.37), quite some variation in parameter estimates (only 13 non-zero values across plot groups)
Salix glauca
model_out.shrub_gradient.SalGla <- jags(shrub_gradient_jags.SalGla.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.SalGla) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.SalGla <- model_out.shrub_gradient.SalGla$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.SalGla),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.SalGla <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list)-4)){
ci_90.SalGla[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.SalGla[param, 3] <- names(data.frame(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.SalGla <- coeff.shrub_gradient.SalGla %>%
left_join(ci_90.SalGla, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.SalGla, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_SalGla.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_SalGla.Rdata"))
# effect size plot
(effect_size_plot.SalGla <- model_plot_sig_function(coeff.shrub_gradient.SalGla, title_string = "Salix glauca", plot_width = 12.5))
- positive response to competition (sig.), negative response to other shrub cover (m.s.)
- slightly negative response to temperature & solar radiation (n.s.)
As the quadratic terms for summer temperature, temperature variability and summer precipitation were not significant, they were removed from the model before re-running:
# new model object with terms removed
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
# b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
# b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
# b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
# b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
# b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
# b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
# b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.SalGla2.jags"))
# specify new set of parameters to be monitored
params_SalGla2 <- c("intercept",
"b.tempjja.x", # "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet", "phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_tri", "phat_twi",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
model_out.shrub_gradient.SalGla2 <- jags(shrub_gradient_jags.SalGla.data, # input data
inits = NULL, # JAGS to create initial values
params_SalGla2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.SalGla2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.SalGla2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.SalGla2 <- model_out.shrub_gradient.SalGla2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# add 90% CIs
ci_90.SalGla2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list)-4)){
ci_90.SalGla2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalGla2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.SalGla2[param, 3] <- names(data.frame(model_out.shrub_gradient.SalGla2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.SalGla2 <- coeff.shrub_gradient.SalGla2 %>%
left_join(ci_90.SalGla2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.SalGla2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalGla2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalGla2.Rdata"))
# effect size plot
(effect_size_plot.SalGla2 <- model_plot_marg_function(coeff.shrub_gradient.SalGla2, title_string = "Salix glauca", plot_width = 9.5))
- positive response to competition (sig.) and temperature variability (linear, m.s.); negative response to other shrub cover (m.s.)
- positive response to moisture & terrain heterogeneity, negative response to solar radiation (n.s.)
Vaccinium uliginosum
model_out.shrub_gradient.VacUli <- jags(shrub_gradient_jags.VacUli.data, # input data
inits = NULL, # JAGS to create initial values
params, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.VacUli) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.VacUli <- model_out.shrub_gradient.VacUli$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.VacUli),"[[]",fixed=FALSE), "[", 1))) #%>% print
# add 90% CIs
ci_90.VacUli <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list)-4)){
ci_90.VacUli[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.VacUli[param, 3] <- names(data.frame(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.VacUli <- coeff.shrub_gradient.VacUli %>%
left_join(ci_90.VacUli, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.VacUli, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_VacUli.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_VacUli.Rdata"))
# effect size plot
(effect_size_plot.VacUli <- model_plot_marg_function(coeff.shrub_gradient.VacUli, title_string = "Vaccinium uliginosum", plot_width = 12.5))
- unimodal relationship with summer temperature (sig.), negative relationship with competition (m.s.)
As the quadratic terms for summer precipitation and temperature variability were not significant, they were removed from the model before re-running:
# new model object with terms removed
write("
model{
# priors
intercept ~ dnorm(0, 0.0001)
b.compet ~ dnorm(0, 0.0001)
b.shrub_cov ~ dnorm(0, 0.0001)
b.gramin_cov ~ dnorm(0, 0.0001)
b.sri ~ dnorm(0, 0.0001)
b.tri ~ dnorm(0, 0.0001)
b.twi ~ dnorm(0, 0.0001)
sigma.plotgroup ~ dunif(0,100)
tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
b.tempjja.x ~ dnorm(0, 0.001)
b.tempjja.x2 ~ dnorm(0, 0.001)
b.tempcont.x ~ dnorm(0, 0.001)
# b.tempcont.x2 ~ dnorm(0, 0.001)
b.precipjja.x ~ dnorm(0, 0.001)
# b.precipjja.x2 ~ dnorm(0, 0.001)
b.tempXtwi ~ dnorm(0, 0.001)
b.tempXtwi2 ~ dnorm(0, 0.001)
phi ~ dgamma(0.1, 0.1)
# LIKELIHOOD for discrete part
for (i in 1:N_discrete){
cov.dis[i] ~ dbern(mu[i])
logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
b.compet * compet.dis[i] +
b.shrub_cov * shrub_cov.dis[i] +
b.gramin_cov * gramin_cov.dis[i] +
b.tempXtwi * tempjja.dis[i] * twi.dis[i] + # for interaction
b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] + # for interaction
b.twi * twi.dis[i] +
b.sri * sri.dis[i] +
b.tri * tri.dis[i]
}
# LIKELIHOOD for continuous part
for (j in 1:N_cont){
cov.cont[j] ~ dbeta(p[j], q[j])
p[j] <- mu2[j] * phi
q[j] <- (1 - mu2[j]) * phi
logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
b.compet * compet.cont[j] +
b.shrub_cov * shrub_cov.cont[j] +
b.gramin_cov * gramin_cov.cont[j] +
b.tempXtwi * tempjja.cont[j] * twi.cont[j] + # for interaction
b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] + # for interaction
b.twi * twi.cont[j] +
b.sri * sri.cont[j] +
b.tri * tri.cont[j]
}
for (k in 1:N_plotgroups){ # length of total plotgroups
b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
mu.plotgroup[k] <- intercept +
# plot group level predictors, linear and quadratic term
b.tempjja.x * tempjja.tot[k] +
b.tempjja.x2 * (tempjja.tot[k]^2) +
b.tempcont.x * tempcont.tot[k] +
# b.tempcont.x2 * (tempcont.tot[k]^2) +
b.precipjja.x * precipjja.tot[k] # +
# b.precipjja.x2 * (precipjja.tot[k]^2)
}
# add predicted values (derived parameters)
for (m in 1:Nxhat){
phat_compet[m] <- intercept + b.compet * xhat_compet[m]
phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
phat_sri[m] <- intercept + b.sri * xhat_sri[m]
phat_tri[m] <- intercept + b.tri * xhat_tri[m]
phat_twi[m] <- intercept + b.twi * xhat_twi[m]
phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
for (p in 1:Nxhat2){
phat_tempXmoist[m,p] <- intercept +
b.tempjja.x * xhat_tempjja[m] +
b.tempjja.x2 * (xhat_tempjja[m]^2) +
b.twi * xhat_twi2[p] +
b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
}
}
}
", file.path("..", "models_twi", "shrub_gradient.VacUli2.jags"))
# specify new set of parameters to be monitored
params_VacUli2 <- c("intercept",
"b.tempjja.x", # "b.tempjja.x2",
"b.tempcont.x", # "b.tempcont.x2",
"b.precipjja.x", # "b.precipjja.x2",
"b.compet",
"b.shrub_cov",
"b.gramin_cov",
"b.sri",
"b.tri",
"b.twi",
"b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
"sigma.plotgroup",
"phi",
"phat_compet", "phat_shrub_cover", "phat_graminoid_cover",
"phat_sri", "phat_tri", "phat_twi",
"phat_tempjja", "phat_tempcont", "phat_precipjja",
"phat_tempXmoist")
model_out.shrub_gradient.VacUli2 <- jags(shrub_gradient_jags.VacUli.data, # input data
inits = NULL, # JAGS to create initial values
params_VacUli2, # parameters to be saved
model.file = file.path("..", "models_twi", "shrub_gradient.VacUli2.jags"),
n.chains = 3, # no. Markov chains
n.iter = 100000, n.burnin = 70000, # no. iterations & burn-in fraction per chain
n.thin = 2, # thinning rate
DIC = FALSE, # do not compute deviance, pD, and DIC
working.directory = NULL,
progress.bar = "text")
# plot(model_out.shrub_gradient.VacUli2) #check convergence, etc.Extract coefficients and plot effect sizes:
# extract coefficients
coeff.shrub_gradient.VacUli2 <- model_out.shrub_gradient.VacUli2$BUGSoutput$summary %>%
as.data.frame %>%
select('mean','sd','2.5%','97.5%','Rhat') %>%
# add identifying info to data frame
rownames_to_column(var = "param")
# add 90% CIs
ci_90.VacUli2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list)-4)){
ci_90.VacUli2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
ci_90.VacUli2[param, 3] <- names(data.frame(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list))[param]
}
# join to coefficients table
coeff.shrub_gradient.VacUli2 <- coeff.shrub_gradient.VacUli2 %>%
left_join(ci_90.VacUli2, by = "param") %>%
# reorder and rename cols
select(param, mean, sd,
l95 = "2.5%",
l90 = q5,
u90 = q95,
u95 = "97.5%",
Rhat) %>% print
save(coeff.shrub_gradient.VacUli2, file = file.path("..", "data", "processed", "model_outputs", "model_output_VacUli2.Rdata"))
# effect size plot
(effect_size_plot.VacUli2 <- model_plot_marg_function(coeff.shrub_gradient.VacUli2, title_string = "Vaccinium uliginosum", plot_width = 9.5))
- negative relationship with competition (m.s.)
- negative relationship with moisture, positive response to solar radiation (n.s.)
---
title: 'Temperature variability, moisture and biotic interactions drive shrub species abundance along a coastal-inland gradient in arctic Greenland'
subtitle: <center>- Analysis workflow -</center>
author: "Jonathan von Oppen (jonathan.vonoppen@bio.au.dk) et al."
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_notebook:
    code_folding: hide
    df_print: paged
    toc: yes
    toc_float:
      collapsed: no
  html_document:
    df_print: paged
    toc: yes
editor_options: 
  chunk_output_type: inline
---

```{r}
rm(list = ls())
```


## Dependencies
Packages included:

* tidyverse - for convenient code flow, data wrangling and plotting
* rjags & R2jags - to link JAGS and R
* skimr - for data summary
* corrr - to output correlation matrices in dataframe format
* cowplot & patchwork - to combine plot panels
* Rmarkdown - to produce this neat little documentation
```{r setup, message = FALSE}
# load pacman package from the repository, if you do not already have it
if (!require('pacman')) install.packages('pacman', repos="https://cloud.r-project.org")
pacman::p_load(tidyverse, # set of packages for data manipulation, exploration and visualisation
               rjags,     # to link JAGS and R
               R2jags,    # to link JAGS and R
               skimr,     # for quick dataframe inspection
               corrr,     # output correlation matrices as data frame
               cowplot,   # combine plot panels
               patchwork, # -"-
               rmarkdown) # for R Markdown formatting
```

Functions used: 

* effect size plot function
```{r, echo = FALSE}
# for cases with only 'significant' effects
model_plot_sig_function <- function(model_coeff_output, title_string, plot_width) {
  target_vars <- c("b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", "b.tempcont.x2",
                   "b.precipjja.x", "b.precipjja.x2",
                   "b.sri",
                   "b.tri",
                   "b.twi", 
                   "b.shrub_cov",
                   "b.gramin_cov",
                   "b.compet")
  solutions <- model_coeff_output
  names(solutions) <- c("variable", "post.mean", "post.sd", "l95", "l90", "u90", "u95", "Rhat")
  solutions <- solutions %>% 
    filter(variable %in% target_vars)
  # solutions$variable <- factor(solutions$variable,
  #                               levels = c("b.tempjja.x", "b.tempjja.x2",
  #                                          "b.tempcont.x", "b.tempcont.x2",
  #                                          "b.precipjja.x", "b.precipjja.x2",
  #                                          "b.sri",
  #                                          "b.tri",
  #                                          "b.twi",
  #                                          "b.compet"))
  min_value <- floor(min(solutions$l95))
  max_value <- ceiling(max(solutions$u95))
  solutions$sig <- "ns"
  solutions$sig[solutions$l95 < 0 & solutions$u95 < 0] <- "sig"
  solutions$sig[solutions$l95 > 0 & solutions$u95 > 0] <- "sig"
  label_colour <- rep("black", nrow(solutions))
  label_colour[solutions$sig == "sig"] <- theme_darkgreen
  label_face <- rep("plain", nrow(solutions))
  label_face[solutions$sig == "sig"] <- "bold"
  # label_face[response == "T1_mean" & solutions$sig == "sig"] <- "bold"
  title_string <- title_string
  title_colour <- "grey10"
  # if(response == "T1_mean" | response == "T1_amp") title_colour <- theme_red
  # if(response == "T2_mean" | response == "T2_amp") title_colour <- theme_yellow
  # if(response == "T1_mean") response <- "Soil"
  # if(response == "T2_mean") response <- "Ground"
  
  
  model_plot_sig <- ggplot(solutions, aes(x = variable, y = post.mean,
                                      ymin = l95, ymax = u95,
                                      colour = sig)) +
    geom_point() +
    geom_errorbar(width = .8) +
    theme_cowplot(18) +
    ylab("Effect Size (scaled)") +
    xlab("") +
    ggtitle(paste0(title_string)) +
    scale_colour_manual(values = c("black", theme_darkgreen)) +
    scale_y_continuous(limits = c(min_value, max_value), breaks = seq(min_value,max_value,0.5)) +
    # scale_x_discrete(limits = c("b.tempjja.x", "b.tempjja.x2",
    #         "b.tempcont.x", "b.tempcont.x2",
    #         "b.precipjja.x", "b.precipjja.x2",
    #         "b.sri",
    #         "b.tri",
    #         "b.twi",
    #         "b.compet"),
    #                  labels = c("summer temperature", bquote(.("summer") *" "* temperature^2),
    #                             "temperature variability", bquote(.("temperature") *" "* variability^2),
    #                             "summer precipitation", bquote(.("summer") *" "* precipitation^2),
    #                             "solar radiation",
  #                             "terrain ruggedness",
  #                             "moisture availability",
  #                             "competition")) +
  annotate("segment", x = 0, xend = plot_width, y = 0, yend = 0) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = label_colour, face = label_face),
          plot.title = element_text(colour = title_colour, face = "italic"),
          legend.position = "none")
  return(model_plot_sig)
}

# for cases with marginal 'significance'
model_plot_marg_function <- function(model_coeff_output, title_string, plot_width) {
  target_vars <- c("b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", "b.tempcont.x2",
                   "b.precipjja.x", "b.precipjja.x2",
                   "b.sri",
                   "b.tri",
                   "b.twi", 
                   "b.shrub_cov",
                   "b.gramin_cov",
                   "b.compet")
  solutions <- model_coeff_output
  names(solutions) <- c("variable", "post.mean", "post.sd", "l95", "l90", "u90", "u95", "Rhat")
  solutions <- solutions %>% 
    filter(variable %in% target_vars)
  # solutions$variable <- factor(solutions$variable,
  #                               levels = c("b.tempjja.x", "b.tempjja.x2",
  #                                          "b.tempcont.x", "b.tempcont.x2",
  #                                          "b.precipjja.x", "b.precipjja.x2",
  #                                          "b.sri",
  #                                          "b.tri",
  #                                          "b.twi",
  #                                          "b.compet"))
  min_value <- floor(min(solutions$l95))
  max_value <- ceiling(max(solutions$u95))
  solutions$sig <- "ns"
  solutions$sig[solutions$l95 < 0 & solutions$u95 < 0] <- "sig"
  solutions$sig[solutions$l95 > 0 & solutions$u95 > 0] <- "sig"
  solutions$sig[solutions$l90 < 0 & solutions$u90 < 0 & solutions$l95 < 0 & solutions$u95 > 0] <- "marg"
  solutions$sig[solutions$l90 > 0 & solutions$u90 > 0 & solutions$l95 < 0 & solutions$u95 > 0] <- "marg"
  label_colour <- rep("black", nrow(solutions))
  label_colour[solutions$sig == "sig"] <- theme_darkgreen
  label_colour[solutions$sig == "marg"] <- theme_purple
  label_face <- rep("plain", nrow(solutions))
  label_face[solutions$sig == "sig"] <- "bold"
  # label_face[response == "T1_mean" & solutions$sig == "sig"] <- "bold"
  title_string <- title_string
  title_colour <- "grey10"
  # if(response == "T1_mean" | response == "T1_amp") title_colour <- theme_red
  # if(response == "T2_mean" | response == "T2_amp") title_colour <- theme_yellow
  # if(response == "T1_mean") response <- "Soil"
  # if(response == "T2_mean") response <- "Ground"
  
  
  model_plot_marg <- ggplot(solutions, aes(x = variable, y = post.mean,
                                      ymin = l95, ymax = u95,
                                      colour = sig)) +
    geom_point() +
    geom_errorbar(width = .8) +
    theme_cowplot(18) +
    ylab("Effect Size (scaled)") +
    xlab("") +
    ggtitle(paste0(title_string)) + 
    scale_colour_manual(values = c(theme_purple, "black", theme_darkgreen)) +
    scale_y_continuous(limits = c(min_value, max_value), breaks = seq(min_value,max_value,0.5)) +
    # scale_x_discrete(limits = c("b.tempjja.x", "b.tempjja.x2",
    #         "b.tempcont.x", "b.tempcont.x2",
    #         "b.precipjja.x", "b.precipjja.x2",
    #         "b.sri",
    #         "b.tri",
    #         "b.twi",
    #         "b.compet"),
    #                  labels = c("summer temperature", bquote(.("summer") *" "* temperature^2),
    #                             "temperature variability", bquote(.("temperature") *" "* variability^2),
    #                             "summer precipitation", bquote(.("summer") *" "* precipitation^2),
    #                             "solar radiation",
  #                             "terrain ruggedness",
  #                             "moisture availability",
  #                             "competition")) +
  annotate("segment", x = 0, xend = plot_width, y = 0, yend = 0) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = label_colour, face = label_face),
          plot.title = element_text(colour = title_colour, face = "italic"),
          legend.position = "none")
  return(model_plot_marg)
}

```

Colour scheme:
```{r}
theme_darkgreen <- "#13944D"
theme_purple <- "#8757B3"
```

## Overview

This project aims to disentangle drivers of abundance of different shrub species and functional groups in arctic tundra.

Analyses will integrate two levels of variation: **plot group $\times$ taxon level** for climatic predictors (derived from downscaled CHELSA data), due to their coarser (90m) resolution, and **plot $\times$ taxon level** for plot-level  topographic and biotic interaction predictors. 

The total dataset comprises 4968 observations for 66 variables (data compilation is documented in `scripts/jonathan/nuuk_shrub_drivers_data_compilation.R`):

```{r}
env_cov_bio <- read.csv(file.path("..", "data", "processed", "nuuk_env_cover_plots.csv"), header = T) %>% 
  # rename variable "twi_90m" to "twi"
  rename(twi = twi_90m)
```
<!-- <button class="btn btn-primary" data-toggle="collapse" data-target="#BlockName"> Show/Hide Data Summary </button> -->
<!-- <div id="BlockName" class="collapse"> -->
<!-- ```{r} -->
<!-- skim(env_cov_bio) -->
<!-- ``` -->


## Data preparation for JAGS models 
### a) selection of variables relevant for analysis
including predictors

* Information on site, plot, plot group, sampling location (lat, lon, altitude), sampling year 
* downscaled CHELSA predictors, averaged over a 30-year period (*[...]_ts_30*) 
* solar radiation index (SRI, following [Keating et al. 2007](http://www.bioone.org/doi/abs/10.2193/2006-359)), slope (erosion measure), Terrain Ruggedness Index (TRI, following [Riley et al. 1999](http://download.osgeo.org/qgis/doc/reference-docs/Terrain_Ruggedness_Index.pdf)), Normalised Difference Wetness Index (NDWI, [Gao 1996](https://www.sciencedirect.com/science/article/pii/S0034425796000673)), Tasseled Cap Wetness Index (TCWI, [Crist & Ciccone 1984](https://ieeexplore.ieee.org/document/4157507)), SAGA Topographic Wetness Index (TWI, see [Conrad et al. 2015](https://gmd.copernicus.org/articles/8/1991/2015/) and the [index documentation](http://www.saga-gis.org/saga_tool_doc/2.2.2/ta_hydrology_15.html))
* cover of other shrub species (only used in species models)
* cover of graminoids
* competition pressure in the community (as summed abundance of taller-growing shrub species within a plot, averaged within plot groups)
* taxon (see below for levels)

and response variable 

* cover (as relative no. hits per plot for each species/group)
```{r}
env_cov_bio_sub <- env_cov_bio %>% 
  select(site_alt_plotgroup_id, plot, site, site_alt_id, year, long, lat, alt,  # plot info / metadata
  ends_with("_ts_30"),        # CHELSA predictors averaged over 30-year period prior to study year
  inclin_down, sri, twi, tri,# environmental data
  ends_with("_cover"), compet,# biotic predictors: shrub & cover, competition pressure
  taxon, cover)               # taxon, cover response
head(env_cov_bio_sub)
```

Let's check for correlation between the different moisture predictors and terrain ruggedness:

```{r, warning = FALSE}
(cor_moist <- env_cov_bio %>% 
  select(twi, ndwi, tcws, tri)) %>% 
  correlate(diagonal = 1)
```
$\Rightarrow$ NDWI and TCWS are highly correlated, but both are largely independent from TWI and TRI.
We'll go with TCWS for now, as it is perhaps the most evidence-based moisture predictor (TWI on the other hand should be least confounded by vegetation, but is also rather theoretical).

<br>

Predictors don't always vary between plots within plotgroups - perhaps due to several falling into the same CHELSA grid cell.

Example: plots within site 1, altitude 20, plot group 1: plot P146 is slightly off and therefore has different climate variables than the other ones

Therefore plotgroup-scale (climatic) predictors will have to be averaged before using in models!

```{r}
env_cov_bio %>% filter(taxon == "Betula nana" & site_alt_plotgroup_id == "1_20_1") %>% 
  ggplot(aes(x = long, y = lat)) + 
  geom_point() + 
  geom_text(aes(label = plot), hjust = 0.0001) + 
  xlim(c(-51.78675, -51.7863))
env_cov_bio %>% filter(taxon == "Betula nana" & site_alt_plotgroup_id == "1_20_1") %>% 
  select(site_alt_plotgroup_id, plot, tempjja_ts_30)
```

Check for correlation between predictors:

```{r, warning = FALSE}
predictors_set <- env_cov_bio_sub %>% 
  select(ends_with("_ts_30"),   # CHELSA predictors averaged over 10-year period prior to study year
         inclin_down, sri, tri, twi,      # environmental data
         shrub_cover, graminoid_cover, compet) %>%  # biotic predictors
  names()
# create basic correlation matrix
(cor_mat <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set) %>% 
  correlate(diagonal = 1) %>% 
  # drop all values < .4 to increase readability
  mutate_if(is.numeric, ~ ifelse(abs(.) < .4, NA, .)))
```

$\Rightarrow$ Maximum temperature is highly correlated with summer temperature, minimum temperature with continentality, slope (inclin_down) with solar radiation, and spring precipitation variables with summer precipitation.
Let's exclude them step by step and check the variance inflation factors (VIF) along the way. VIF values > 5 indicate collinearity issues.

```{r}
# for the whole set of predictors
(vif_predictors_1 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set) %>% 
  usdm::vif()) #%>% View()
# => drop tempmax (correlated w/ tempjja)
(vif_predictors_2 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set,
                -tempmax_ts_30) %>% 
  usdm::vif()) #%>% View()
# => drop tempmin (correlated w/ tempcont)
(vif_predictors_3 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set,
                -tempmax_ts_30,
                -tempmin_ts_30) %>% 
  usdm::vif()) #%>% View()
# => drop precipjfmam (correlated w/ precipjja & tempcont)
(vif_predictors_4 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set,
                -tempmax_ts_30,
                -tempmin_ts_30,
                -contains("jfm")) %>% 
  usdm::vif()) #%>% View()
# => drop inclin_down (correlated w/ SRI)
(vif_predictors_5 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set,
                -tempmax_ts_30,
                -tempmin_ts_30,
                -inclin_down,
                -contains("jfm")) %>% 
  usdm::vif()) #%>% View()
# => drop precipmam (correlated w/ precipjja)
(vif_predictors_6 <- env_cov_bio_sub %>% 
  dplyr::select(predictors_set,
                -tempmax_ts_30,
                -tempmin_ts_30,
                -inclin_down,
                -contains("mam")) %>% 
  usdm::vif()) #%>% View()
```

All VIF are now < 3 -> OK! We can now exclude the dropped variables to obtain the final dataset. In addition, we rename the climate variables and delete the extension:

```{r}
env_cov_bio_sub <- env_cov_bio_sub %>% 
  dplyr::select(-tempmax_ts_30,
                -tempmin_ts_30,
                -inclin_down,
                -contains("mam")) %>% 
  
  rename(tempjja = tempjja_ts_30,
         tempcont = tempcont_ts_30,
         precipjja = precipjja_ts_30)
str(env_cov_bio_sub)
```

### b) adjustion of data structure

Data is ordered by site/altitude/plotgroup and taxon
```{r}
env_cov_bio_sub <- env_cov_bio_sub[order(env_cov_bio_sub$site_alt_plotgroup_id, env_cov_bio_sub$taxon),]

```

As JAGS is only able to handle numeric input, all variables are assigned a numeric identifier:
```{r}
env_cov_bio_sub$plotgroup.NUM <- as.numeric(factor(env_cov_bio_sub$site_alt_plotgroup_id,
                                                   levels = unique(env_cov_bio_sub$site_alt_plotgroup_id)))
env_cov_bio_sub$plot.NUM <- as.numeric(factor(env_cov_bio_sub$plot,
                                              levels = unique(env_cov_bio_sub$plot)))
env_cov_bio_sub$site_alt.NUM <- as.numeric(factor(env_cov_bio_sub$site_alt_id,
                                              levels = unique(env_cov_bio_sub$site_alt_id)))
env_cov_bio_sub$site.NUM <- as.numeric(factor(env_cov_bio_sub$site, levels = unique(env_cov_bio_sub$site)))
env_cov_bio_sub$taxon.NUM <- as.numeric(factor(env_cov_bio_sub$taxon, levels = unique(env_cov_bio_sub$taxon)))
```

Taxa were coded as follows: 
```{r}
data.frame(taxon = levels(env_cov_bio_sub$taxon),
           num = unique(env_cov_bio_sub$taxon.NUM))
```

Numeric predictors were scaled and centered: 
```{r}
num_pred <- env_cov_bio_sub %>% select(tempjja,
                                       tempcont,
                                       precipjja,
                                       sri, 
                                       twi,
                                       tri, 
                                       ends_with("_cover"),
                                       matches("compet"))
for(i in 1:length(num_pred)){
  col <- colnames(num_pred[i])
  env_cov_bio_sub[paste0(col,"C")] <- as.numeric(scale(num_pred[i], scale = TRUE, center = TRUE))
}
```

To account for the range of the cover response ($0 \leq cover \leq 1$), the model needs a mixed structure incorporating a beta distribution (for all continuous values with $0 < cover < 1$) and a binomial distribution (for all discrete values of $cover = \{0, 1\}$). An additional variable *cover_discrete* was introduced to separate the dataset into discrete (= 1) and continuous (= 0) cover values:
```{r}
env_cov_bio_sub$cover_discrete <- ifelse(env_cov_bio_sub$cover == 1 | env_cov_bio_sub$cover == 0, 1, 0)
```


<br>

## JAGS models
The dataset was then ready to be split up into the species of interest. We create separate data subsets for all/discrete/continuous response variable values for each species:
```{r}
# split dataframe by taxon
env_cov_bio_sub_spec.tot <- split(env_cov_bio_sub, env_cov_bio_sub$taxon)

# assign taxon name to list elements
# >> for total datasets
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
  # extract 3-letter genus name string
  assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id], 
                            "^\\w{3}"),
  # extract and capitalise 3-letter species name string
                str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
                                                    "\\s\\w{3}"),
                                        "\\s")),
  # add extension
                ".tot"),
  # assign to respective list element
         env_cov_bio_sub_spec.tot[[taxon_id]])
}



# >> for discrete datasets
env_cov_bio_sub_spec.dis <- list()
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
  # filter for discrete response values
  env_cov_bio_sub_spec.dis[[taxon_id]] <- filter(env_cov_bio_sub_spec.tot[[taxon_id]], cover_discrete == 1)
  # extract 3-letter genus name string
  assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id], 
                            "^\\w{3}"),
  # extract and capitalise 3-letter species name string
                str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
                                                    "\\s\\w{3}"),
                                        "\\s")),
  # add extension
                ".dis"),
  # assign to respective list element
         env_cov_bio_sub_spec.dis[[taxon_id]])
}

# >> for continuous datasets
env_cov_bio_sub_spec.cont <- list()
for (taxon_id in 1:nlevels(env_cov_bio_sub$taxon)){
  # filter for continuous response values
  env_cov_bio_sub_spec.cont[[taxon_id]] <- filter(env_cov_bio_sub_spec.tot[[taxon_id]], cover_discrete == 0)
  # extract 3-letter genus name string
  assign(paste0(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id], 
                            "^\\w{3}"),
  # extract and capitalise 3-letter species name string
                str_to_title(str_remove(str_extract(levels(env_cov_bio_sub$taxon)[taxon_id],
                                                    "\\s\\w{3}"),
                                        "\\s")),
  # add extension
                ".cont"),
  # assign to respective list element
         env_cov_bio_sub_spec.cont[[taxon_id]])
}

# # save all input data
# # groups
# save(list = c("AllShr.tot",
#               "AllEve.tot",
#               "AllDec.tot"), 
#      file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_group.datasets.Rdata"))
# 
# # species
# # groups
# save(list = c("BetNan.tot",
#               "CasTet.tot",
#               "EmpNig.tot",
#               "PhyCae.tot",
#               "RhoGro.tot",
#               "RhoTom.tot",
#               "SalArc.tot",
#               "SalGla.tot",
#               "VacUli.tot"), 
#      file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_species.datasets.Rdata"))

```

### Groups
### > assembling data for model input in lists

JAGS needs data input in list format, so all relevant variables are compiled into lists:
```{r}
# Compile data into lists
# All shrubs ----
shrub_gradient_jags.AllShr.data <- list(
  
  # plot level predictors
  cov.tot = AllShr.tot$cover + 1,
  plotgroup.tot = AllShr.tot$plotgroup.NUM,
  sri.tot = AllShr.tot$sriC,
  tri.tot = AllShr.tot$triC,
  twi.tot = AllShr.tot$twiC,
  gramin_cov.tot = AllShr.tot$graminoid_coverC,
  tempjja.tot.plot = AllShr.tot$tempjjaC,
  N_plots = length(unique(AllShr.tot$plot)),
  
  # plot group level predictors
  tempjja.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = AllShr.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(AllShr.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_graminoid_cover = seq(from = min(AllShr.tot$graminoid_coverC), to = max(AllShr.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(AllShr.tot$sriC), to = max(AllShr.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(AllShr.tot$triC), to = max(AllShr.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(AllShr.tot$twiC), to = max(AllShr.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(AllShr.tot$tempjjaC), to = max(AllShr.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(AllShr.tot$precipjjaC), to = max(AllShr.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(AllShr.tot$tempcontC), to = max(AllShr.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(AllShr.tot$twiC,0.05),quantile(AllShr.tot$twiC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.AllShr.data)

# All evergreen shrubs ----
shrub_gradient_jags.AllEve.data <- list(
  
  # plot level predictors
  cov.tot = AllEve.tot$cover + 1,
  plotgroup.tot = AllEve.tot$plotgroup.NUM,
  sri.tot = AllEve.tot$sriC,
  tri.tot = AllEve.tot$triC,
  twi.tot = AllEve.tot$twiC,
  gramin_cov.tot = AllEve.tot$graminoid_coverC,
  tempjja.tot.plot = AllEve.tot$tempjjaC,
  N_plots = length(unique(AllEve.tot$plot)),
  
  # plot group level predictors
  tempjja.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = AllEve.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(AllEve.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_graminoid_cover = seq(from = min(AllEve.tot$graminoid_coverC), to = max(AllEve.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(AllEve.tot$sriC), to = max(AllEve.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(AllEve.tot$triC), to = max(AllEve.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(AllEve.tot$twiC), to = max(AllEve.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(AllEve.tot$tempjjaC), to = max(AllEve.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(AllEve.tot$precipjjaC), to = max(AllEve.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(AllEve.tot$tempcontC), to = max(AllEve.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(AllEve.tot$twiC,0.05),quantile(AllEve.tot$twiC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.AllEve.data)

# All decidious shrubs ----
shrub_gradient_jags.AllDec.data <- list(
  
  # plot level predictors
  cov.tot = AllDec.tot$cover + 1,
  plotgroup.tot = AllDec.tot$plotgroup.NUM,
  sri.tot = AllDec.tot$sriC,
  tri.tot = AllDec.tot$triC,
  twi.tot = AllDec.tot$twiC,
  gramin_cov.tot = AllDec.tot$graminoid_coverC,
  tempjja.tot.plot = AllDec.tot$tempjjaC,
  N_plots = length(unique(AllDec.tot$plot)),
  
  # plot group level predictors
  tempjja.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = AllDec.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(AllDec.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_graminoid_cover = seq(from = min(AllDec.tot$graminoid_coverC), to = max(AllDec.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(AllDec.tot$sriC), to = max(AllDec.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(AllDec.tot$triC), to = max(AllDec.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(AllDec.tot$twiC), to = max(AllDec.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(AllDec.tot$tempjjaC), to = max(AllDec.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(AllDec.tot$precipjjaC), to = max(AllDec.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(AllDec.tot$tempcontC), to = max(AllDec.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(AllDec.tot$twiC,0.05),quantile(AllDec.tot$twiC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.AllDec.data)


# # save model input data
# shrub_gradient_jags.groupdata <- list
# 
# save(list = c("shrub_gradient_jags.AllShr.data",
#               "shrub_gradient_jags.AllEve.data",
#               "shrub_gradient_jags.AllDec.data"), 
#      file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_jags.groupdata.Rdata"))
```

### > specifying model
```{r}
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plot ~ dunif(0,100)
      tau.plot <- 1/(sigma.plot * sigma.plot)
      
      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      
    # plot level

      for (i in 1:N_plots){ 
        cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
        log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
                        b.gramin_cov * gramin_cov.tot[i] + 
                        b.twi * twi.tot[i] + 
                        b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] +  # for interaction
                        b.sri * sri.tot[i] +
                        b.tri * tri.tot[i]
      }


    # plot group level
    
      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] + 
                    b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
        }
      }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.groups.jags"))
```

Specify the parameters to be monitored:
```{r}
params_groups <- c("intercept",
                   "b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", "b.tempcont.x2",
                   "b.precipjja.x", "b.precipjja.x2",
                   "b.gramin_cov",
                   "b.sri",
                   "b.tri",
                   "b.twi",
                   "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                   "sigma.plotgroup",
                   "phat_graminoid_cover", 
                   "phat_sri", "phat_twi", "phat_tri", 
                   "phat_tempjja", "phat_tempcont", "phat_precipjja",
                   "phat_tempXmoist")
```

### > run & evaluate model
<br>

### *All shrubs*
```{r}
# run model
model_out.shrub_gradient.AllShr <- jags(shrub_gradient_jags.AllShr.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_groups,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllShr) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllShr <- model_out.shrub_gradient.AllShr$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllShr),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllShr <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list)-4)){
  ci_90.AllShr[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllShr[param, 3] <- names(data.frame(model_out.shrub_gradient.AllShr$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllShr <- coeff.shrub_gradient.AllShr %>% 
  left_join(ci_90.AllShr, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllShr, 
     file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllShr.Rdata"))

(effect_size_plot.AllShr <- model_plot_sig_function(coeff.shrub_gradient.AllShr, title_string = "all shrubs", plot_width = 10.5))
```

* **unimodal response to summer temperature, negative response to graminoid cover (sig.)**
* slightly positive response to solar radiation (n.s.)

<br>

As precipitation and temp. variability quadratic terms didn't come out significant, re-run without them:
```{r}
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plot ~ dunif(0,100)
      tau.plot <- 1/(sigma.plot * sigma.plot)
      
      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      
    # plot level

      for (i in 1:N_plots){ 
        cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
        log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
                        b.gramin_cov * gramin_cov.tot[i] + 
                        b.twi * twi.tot[i] + 
                        b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] +  # for interaction
                        b.sri * sri.tot[i] +
                        b.tri * tri.tot[i]
      }


    # plot group level
    
      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
        }
      }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.AllShr2.jags"))

# specify new set of parameters
params_AllShr2 <- c("intercept",
                   "b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", # "b.tempcont.x2",
                   "b.precipjja.x", # "b.precipjja.x2",
                   "b.gramin_cov",
                   "b.sri",
                   "b.tri",
                   "b.twi",
                   "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                   "sigma.plotgroup",
                   "phat_graminoid_cover", 
                   "phat_sri", "phat_twi", "phat_tri", 
                   "phat_tempjja", "phat_tempcont", "phat_precipjja",
                   "phat_tempXmoist")

# run model
model_out.shrub_gradient.AllShr2 <- jags(shrub_gradient_jags.AllShr.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_AllShr2,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.AllShr2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllShr2) #check convergence, etc.
```
Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllShr2 <- model_out.shrub_gradient.AllShr2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllShr2),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllShr2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list)-4)){
  ci_90.AllShr2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllShr2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllShr2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllShr2 <- coeff.shrub_gradient.AllShr2 %>% 
  left_join(ci_90.AllShr2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllShr2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllShr2.Rdata"))

(effect_size_plot.AllShr2 <- model_plot_sig_function(coeff.shrub_gradient.AllShr2, title_string = "all shrubs", plot_width = 8.5))
```

* **positive response to temperature variability, unimodal response to summer temperature, negative response to graminoid cover (sig.)**
* positive response to solar radiation (n.s.)

<br>

### *Evergreen shrubs*
```{r}
# run model
model_out.shrub_gradient.AllEve <- jags(shrub_gradient_jags.AllEve.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_groups,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllEve) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllEve <- model_out.shrub_gradient.AllEve$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllEve),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllEve <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list)-4)){
  ci_90.AllEve[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllEve[param, 3] <- names(data.frame(model_out.shrub_gradient.AllEve$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllEve <- coeff.shrub_gradient.AllEve %>% 
  left_join(ci_90.AllEve, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllEve, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllEve.Rdata"))

(effect_size_plot.AllEve <- model_plot_sig_function(coeff.shrub_gradient.AllEve, title_string = "evergreen shrubs", plot_width = 10.5))
```

* **unimodal response to temperature, negative response to graminoid cover (sig.)**
* slightly positive response to solar radiation (n.s.)

<br>

```{r}
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plot ~ dunif(0,100)
      tau.plot <- 1/(sigma.plot * sigma.plot)
      
      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      
    # plot level

      for (i in 1:N_plots){ 
        cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
        log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
                        b.gramin_cov * gramin_cov.tot[i] + 
                        b.twi * twi.tot[i] + 
                        b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] +  # for interaction
                        b.sri * sri.tot[i] +
                        b.tri * tri.tot[i]
      }


    # plot group level
    
      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
        }
      }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.AllEve2.jags"))

# specify new set of parameters
params_AllEve2 <- c("intercept",
                   "b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", # "b.tempcont.x2",
                   "b.precipjja.x", # "b.precipjja.x2",
                   "b.gramin_cov",
                   "b.sri",
                   "b.tri",
                   "b.twi",
                   "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                   "sigma.plotgroup",
                   "phat_graminoid_cover", 
                   "phat_sri", "phat_twi", "phat_tri", 
                   "phat_tempjja", "phat_tempcont", "phat_precipjja",
                   "phat_tempXmoist")

# run model
model_out.shrub_gradient.AllEve2 <- jags(shrub_gradient_jags.AllEve.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_AllEve2,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.AllEve2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllShr2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllEve2 <- model_out.shrub_gradient.AllEve2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllEve),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllEve2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list)-4)){
  ci_90.AllEve2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllEve2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllEve2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllEve2 <- coeff.shrub_gradient.AllEve2 %>% 
  left_join(ci_90.AllEve2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllEve2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllEve2.Rdata"))

(effect_size_plot.AllEve2 <- model_plot_sig_function(coeff.shrub_gradient.AllEve2, title_string = "evergreen shrubs", plot_width = 8.5))
```
* **unimodal response to temperature, negative response to graminoid cover (sig.)**
* slightly positive response to solar radiation (n.s.)

<br>

### *Deciduous shrubs*
```{r}
# run model
model_out.shrub_gradient.AllDec <- jags(shrub_gradient_jags.AllDec.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_groups,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.groups.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllDec) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllDec <- model_out.shrub_gradient.AllDec$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllDec),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllDec <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list)-4)){
  ci_90.AllDec[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllDec[param, 3] <- names(data.frame(model_out.shrub_gradient.AllDec$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllDec <- coeff.shrub_gradient.AllDec %>% 
  left_join(ci_90.AllDec, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllDec, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "first_runs_converged", "model_output_AllDec.Rdata"))

(effect_size_plot.AllDec <- model_plot_marg_function(coeff.shrub_gradient.AllDec, title_string = "deciduous shrubs", plot_width = 10.5))
```

* **positive/unimodal response to summer temperature & temp. variability, negative response to graminoid cover (sig.)**
* slightly positive response to solar radiation, negative response to terrain heterogeneity (n.s.)

<br>

```{r}
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plot ~ dunif(0,100)
      tau.plot <- 1/(sigma.plot * sigma.plot)
      
      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      
    # plot level

      for (i in 1:N_plots){ 
        cov.tot[i] ~ dlnorm(mu.plot[i], tau.plot)
        log(mu.plot[i]) <- b_plotgroup[plotgroup.tot[i]] + # ~= random effect of plot group
                        b.gramin_cov * gramin_cov.tot[i] + 
                        b.twi * twi.tot[i] + 
                        b.tempXtwi * tempjja.tot.plot[i] * twi.tot[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.tot.plot[i]^2) * twi.tot[i] +  # for interaction
                        b.sri * sri.tot[i] +
                        b.tri * tri.tot[i]
      }


    # plot group level
    
      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
        }
      }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.AllDec2.jags"))

# specify new set of parameters
params_AllDec2 <- c("intercept",
                   "b.tempjja.x", "b.tempjja.x2",
                   "b.tempcont.x", "b.tempcont.x2",
                   "b.precipjja.x", # "b.precipjja.x2",
                   "b.gramin_cov",
                   "b.sri",
                   "b.tri",
                   "b.twi",
                   "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                   "sigma.plotgroup",
                   "phat_graminoid_cover", 
                   "phat_sri", "phat_twi", "phat_tri", 
                   "phat_tempjja", "phat_tempcont", "phat_precipjja",
                   "phat_tempXmoist")

# run model
model_out.shrub_gradient.AllDec2 <- jags(shrub_gradient_jags.AllDec.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_AllDec2,                      # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.AllDec2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.AllDec2) #check convergence, etc.
```
Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.AllDec2 <- model_out.shrub_gradient.AllDec2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.AllDec2),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.AllDec2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list)-4)){
  ci_90.AllDec2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.AllDec2[param, 3] <- names(data.frame(model_out.shrub_gradient.AllDec2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.AllDec2 <- coeff.shrub_gradient.AllDec2 %>% 
  left_join(ci_90.AllDec2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.AllDec2, file = file.path("..", "data", "processed", "model_outputs", "groups_twi", "model_output_AllDec2.Rdata"))

(effect_size_plot.AllDec2 <- model_plot_marg_function(coeff.shrub_gradient.AllDec2, title_string = "deciduous shrubs", plot_width = 9.5))
```
* **positive/unimodal response to temperature variability, positive response to soil moisture, negative response to graminoid cover (sig.)**
* *positive response to solar radiation (m.s.)*
* slightly positive response to temperature, negative response to terrain heterogeneity (n.s.)

<br>

### Species
### > species: assembling data for model input in lists

```{r}
# BetNan ----
shrub_gradient_jags.BetNan.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = BetNan.dis$cover,
  plotgroup.dis = BetNan.dis$plotgroup.NUM,
  tempjja.dis = BetNan.dis$tempjjaC,
  sri.dis = BetNan.dis$sriC,
  tri.dis = BetNan.dis$triC,
  twi.dis = BetNan.dis$twiC,
  shrub_cov.dis = BetNan.dis$BetNan_shrub_coverC,
  gramin_cov.dis = BetNan.dis$graminoid_coverC,
  compet.dis = BetNan.dis$competC,
  N_discrete = nrow(BetNan.dis),
  
  # ...and continuous part of the data
  cov.cont = BetNan.cont$cover,
  plotgroup.cont = BetNan.cont$plotgroup.NUM,
  tempjja.cont = BetNan.cont$tempjjaC,
  sri.cont = BetNan.cont$sriC,
  tri.cont = BetNan.cont$triC,
  twi.cont = BetNan.cont$twiC,
  shrub_cov.cont = BetNan.cont$BetNan_shrub_coverC,
  gramin_cov.cont = BetNan.cont$graminoid_coverC,
  compet.cont = BetNan.cont$competC,
  N_cont = nrow(BetNan.cont),
  
  # plot group level predictors
  tempjja.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = BetNan.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(BetNan.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(BetNan.tot$competC), to = max(BetNan.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(BetNan.tot$BetNan_shrub_coverC), to = max(BetNan.tot$BetNan_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(BetNan.tot$graminoid_coverC), to = max(BetNan.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(BetNan.tot$sriC), to = max(BetNan.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(BetNan.tot$triC), to = max(BetNan.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(BetNan.tot$twiC), to = max(BetNan.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(BetNan.tot$tempjjaC), to = max(BetNan.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(BetNan.tot$precipjjaC), to = max(BetNan.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(BetNan.tot$tempcontC), to = max(BetNan.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(BetNan.tot$twiC,0.05),quantile(BetNan.tot$twiC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.BetNan.data)

# CasTet ----
shrub_gradient_jags.CasTet.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = CasTet.dis$cover,
  plotgroup.dis = CasTet.dis$plotgroup.NUM,
  tempjja.dis = CasTet.dis$tempjjaC,
  sri.dis = CasTet.dis$sriC,
  tri.dis = CasTet.dis$triC,
  twi.dis = CasTet.dis$twiC,
  shrub_cov.dis = CasTet.dis$CasTet_shrub_coverC,
  gramin_cov.dis = CasTet.dis$graminoid_coverC,
  compet.dis = CasTet.dis$competC,
  N_discrete = nrow(CasTet.dis),
  
  # ...and continuous part of the data
  cov.cont = CasTet.cont$cover,
  plotgroup.cont = CasTet.cont$plotgroup.NUM,
  tempjja.cont = CasTet.cont$tempjjaC,
  sri.cont = CasTet.cont$sriC,
  tri.cont = CasTet.cont$triC,
  twi.cont = CasTet.cont$twiC,
  shrub_cov.cont = CasTet.cont$CasTet_shrub_coverC,
  gramin_cov.cont = CasTet.cont$graminoid_coverC,
  compet.cont = CasTet.cont$competC,
  N_cont = nrow(CasTet.cont),
  
  # plot group level predictors
  tempjja.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = CasTet.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(CasTet.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(CasTet.tot$competC), to = max(CasTet.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(CasTet.tot$CasTet_shrub_coverC), to = max(CasTet.tot$CasTet_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(CasTet.tot$graminoid_coverC), to = max(CasTet.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(CasTet.tot$sriC), to = max(CasTet.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(CasTet.tot$triC), to = max(CasTet.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(CasTet.tot$twiC), to = max(CasTet.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(CasTet.tot$tempjjaC), to = max(CasTet.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(CasTet.tot$precipjjaC), to = max(CasTet.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(CasTet.tot$tempcontC), to = max(CasTet.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(CasTet.tot$tempjjaC,0.05),quantile(CasTet.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.CasTet.data)

# EmpNig ----
shrub_gradient_jags.EmpNig.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = EmpNig.dis$cover,
  plotgroup.dis = EmpNig.dis$plotgroup.NUM,
  tempjja.dis = EmpNig.dis$tempjjaC,
  sri.dis = EmpNig.dis$sriC,
  tri.dis = EmpNig.dis$triC,
  twi.dis = EmpNig.dis$twiC,
  shrub_cov.dis = EmpNig.dis$EmpNig_shrub_coverC,
  gramin_cov.dis = EmpNig.dis$graminoid_coverC,
  compet.dis = EmpNig.dis$competC,
  N_discrete = nrow(EmpNig.dis),
  
  # ...and continuous part of the data
  cov.cont = EmpNig.cont$cover,
  plotgroup.cont = EmpNig.cont$plotgroup.NUM,
  tempjja.cont = EmpNig.cont$tempjjaC,
  sri.cont = EmpNig.cont$sriC,
  tri.cont = EmpNig.cont$triC,
  twi.cont = EmpNig.cont$twiC,
  shrub_cov.cont = EmpNig.cont$EmpNig_shrub_coverC,
  gramin_cov.cont = EmpNig.cont$graminoid_coverC,
  compet.cont = EmpNig.cont$competC,
  N_cont = nrow(EmpNig.cont),
  
  # plot group level predictors
  tempjja.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = EmpNig.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(EmpNig.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(EmpNig.tot$competC), to = max(EmpNig.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(EmpNig.tot$EmpNig_shrub_coverC), to = max(EmpNig.tot$EmpNig_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(EmpNig.tot$graminoid_coverC), to = max(EmpNig.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(EmpNig.tot$sriC), to = max(EmpNig.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(EmpNig.tot$triC), to = max(EmpNig.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(EmpNig.tot$twiC), to = max(EmpNig.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(EmpNig.tot$tempjjaC), to = max(EmpNig.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(EmpNig.tot$precipjjaC), to = max(EmpNig.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(EmpNig.tot$tempcontC), to = max(EmpNig.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(EmpNig.tot$tempjjaC,0.05),quantile(EmpNig.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.EmpNig.data)

# PhyCae ----
shrub_gradient_jags.PhyCae.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = PhyCae.dis$cover,
  plotgroup.dis = PhyCae.dis$plotgroup.NUM,
  tempjja.dis = PhyCae.dis$tempjjaC,
  sri.dis = PhyCae.dis$sriC,
  tri.dis = PhyCae.dis$triC,
  twi.dis = PhyCae.dis$twiC,
  shrub_cov.dis = PhyCae.dis$PhyCae_shrub_coverC,
  gramin_cov.dis = PhyCae.dis$graminoid_coverC,
  compet.dis = PhyCae.dis$competC,
  N_discrete = nrow(PhyCae.dis),
  
  # ...and continuous part of the data
  cov.cont = PhyCae.cont$cover,
  plotgroup.cont = PhyCae.cont$plotgroup.NUM,
  tempjja.cont = PhyCae.cont$tempjjaC,
  sri.cont = PhyCae.cont$sriC,
  tri.cont = PhyCae.cont$triC,
  twi.cont = PhyCae.cont$twiC,
  shrub_cov.cont = PhyCae.cont$PhyCae_shrub_coverC,
  gramin_cov.cont = PhyCae.cont$graminoid_coverC,
  compet.cont = PhyCae.cont$competC,
  N_cont = nrow(PhyCae.cont),
  
  # plot group level predictors
  tempjja.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = PhyCae.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(PhyCae.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(PhyCae.tot$competC), to = max(PhyCae.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(PhyCae.tot$PhyCae_shrub_coverC), to = max(PhyCae.tot$PhyCae_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(PhyCae.tot$graminoid_coverC), to = max(PhyCae.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(PhyCae.tot$sriC), to = max(PhyCae.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(PhyCae.tot$triC), to = max(PhyCae.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(PhyCae.tot$twiC), to = max(PhyCae.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(PhyCae.tot$tempjjaC), to = max(PhyCae.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(PhyCae.tot$precipjjaC), to = max(PhyCae.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(PhyCae.tot$tempcontC), to = max(PhyCae.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(PhyCae.tot$tempjjaC,0.05),quantile(PhyCae.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.PhyCae.data)

# RhoGro ----
shrub_gradient_jags.RhoGro.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = RhoGro.dis$cover,
  plotgroup.dis = RhoGro.dis$plotgroup.NUM,
  tempjja.dis = RhoGro.dis$tempjjaC,
  sri.dis = RhoGro.dis$sriC,
  tri.dis = RhoGro.dis$triC,
  twi.dis = RhoGro.dis$twiC,
  shrub_cov.dis = RhoGro.dis$RhoGro_shrub_coverC,
  gramin_cov.dis = RhoGro.dis$graminoid_coverC,
  compet.dis = RhoGro.dis$competC,
  N_discrete = nrow(RhoGro.dis),
  
  # ...and continuous part of the data
  cov.cont = RhoGro.cont$cover,
  plotgroup.cont = RhoGro.cont$plotgroup.NUM,
  tempjja.cont = RhoGro.cont$tempjjaC,
  sri.cont = RhoGro.cont$sriC,
  tri.cont = RhoGro.cont$triC,
  twi.cont = RhoGro.cont$twiC,
  shrub_cov.cont = RhoGro.cont$RhoGro_shrub_coverC,
  gramin_cov.cont = RhoGro.cont$graminoid_coverC,
  compet.cont = RhoGro.cont$competC,
  N_cont = nrow(RhoGro.cont),
  
  # plot group level predictors
  tempjja.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = RhoGro.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(RhoGro.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(RhoGro.tot$competC), to = max(RhoGro.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(RhoGro.tot$RhoGro_shrub_coverC), to = max(RhoGro.tot$RhoGro_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(RhoGro.tot$graminoid_coverC), to = max(RhoGro.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(RhoGro.tot$sriC), to = max(RhoGro.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(RhoGro.tot$triC), to = max(RhoGro.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(RhoGro.tot$twiC), to = max(RhoGro.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(RhoGro.tot$tempjjaC), to = max(RhoGro.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(RhoGro.tot$precipjjaC), to = max(RhoGro.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(RhoGro.tot$tempcontC), to = max(RhoGro.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(RhoGro.tot$tempjjaC,0.05),quantile(RhoGro.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.RhoGro.data)

# RhoTom ----
shrub_gradient_jags.RhoTom.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = RhoTom.dis$cover,
  plotgroup.dis = RhoTom.dis$plotgroup.NUM,
  tempjja.dis = RhoTom.dis$tempjjaC,
  sri.dis = RhoTom.dis$sriC,
  tri.dis = RhoTom.dis$triC,
  twi.dis = RhoTom.dis$twiC,
  shrub_cov.dis = RhoTom.dis$RhoTom_shrub_coverC,
  gramin_cov.dis = RhoTom.dis$graminoid_coverC,
  compet.dis = RhoTom.dis$competC,
  N_discrete = nrow(RhoTom.dis),
  
  # ...and continuous part of the data
  cov.cont = RhoTom.cont$cover,
  plotgroup.cont = RhoTom.cont$plotgroup.NUM,
  tempjja.cont = RhoTom.cont$tempjjaC,
  sri.cont = RhoTom.cont$sriC,
  tri.cont = RhoTom.cont$triC,
  twi.cont = RhoTom.cont$twiC,
  shrub_cov.cont = RhoTom.cont$RhoTom_shrub_coverC,
  gramin_cov.cont = RhoTom.cont$graminoid_coverC,
  compet.cont = RhoTom.cont$competC,
  N_cont = nrow(RhoTom.cont),
  
  # plot group level predictors
  tempjja.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = RhoTom.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(RhoTom.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(RhoTom.tot$competC), to = max(RhoTom.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(RhoTom.tot$RhoTom_shrub_coverC), to = max(RhoTom.tot$RhoTom_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(RhoTom.tot$graminoid_coverC), to = max(RhoTom.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(RhoTom.tot$sriC), to = max(RhoTom.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(RhoTom.tot$triC), to = max(RhoTom.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(RhoTom.tot$twiC), to = max(RhoTom.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(RhoTom.tot$tempjjaC), to = max(RhoTom.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(RhoTom.tot$precipjjaC), to = max(RhoTom.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(RhoTom.tot$tempcontC), to = max(RhoTom.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(RhoTom.tot$tempjjaC,0.05),quantile(RhoTom.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.RhoTom.data)

# SalArc ----
shrub_gradient_jags.SalArc.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = SalArc.dis$cover,
  plotgroup.dis = SalArc.dis$plotgroup.NUM,
  tempjja.dis = SalArc.dis$tempjjaC,
  sri.dis = SalArc.dis$sriC,
  tri.dis = SalArc.dis$triC,
  twi.dis = SalArc.dis$twiC,
  shrub_cov.dis = SalArc.dis$SalArc_shrub_coverC,
  gramin_cov.dis = SalArc.dis$graminoid_coverC,
  compet.dis = SalArc.dis$competC,
  N_discrete = nrow(SalArc.dis),
  
  # ...and continuous part of the data
  cov.cont = SalArc.cont$cover,
  plotgroup.cont = SalArc.cont$plotgroup.NUM,
  tempjja.cont = SalArc.cont$tempjjaC,
  sri.cont = SalArc.cont$sriC,
  tri.cont = SalArc.cont$triC,
  twi.cont = SalArc.cont$twiC,
  shrub_cov.cont = SalArc.cont$SalArc_shrub_coverC,
  gramin_cov.cont = SalArc.cont$graminoid_coverC,
  compet.cont = SalArc.cont$competC,
  N_cont = nrow(SalArc.cont),
  
  # plot group level predictors
  tempjja.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = SalArc.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(SalArc.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(SalArc.tot$competC), to = max(SalArc.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(SalArc.tot$SalArc_shrub_coverC), to = max(SalArc.tot$SalArc_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(SalArc.tot$graminoid_coverC), to = max(SalArc.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(SalArc.tot$sriC), to = max(SalArc.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(SalArc.tot$triC), to = max(SalArc.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(SalArc.tot$twiC), to = max(SalArc.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(SalArc.tot$tempjjaC), to = max(SalArc.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(SalArc.tot$precipjjaC), to = max(SalArc.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(SalArc.tot$tempcontC), to = max(SalArc.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(SalArc.tot$tempjjaC,0.05),quantile(SalArc.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.SalArc.data)

# SalGla ----
shrub_gradient_jags.SalGla.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = SalGla.dis$cover,
  plotgroup.dis = SalGla.dis$plotgroup.NUM,
  tempjja.dis = SalGla.dis$tempjjaC,
  sri.dis = SalGla.dis$sriC,
  tri.dis = SalGla.dis$triC,
  twi.dis = SalGla.dis$twiC,
  shrub_cov.dis = SalGla.dis$SalGla_shrub_coverC,
  gramin_cov.dis = SalGla.dis$graminoid_coverC,
  compet.dis = SalGla.dis$competC,
  N_discrete = nrow(SalGla.dis),
  
  # ...and continuous part of the data
  cov.cont = SalGla.cont$cover,
  plotgroup.cont = SalGla.cont$plotgroup.NUM,
  tempjja.cont = SalGla.cont$tempjjaC,
  sri.cont = SalGla.cont$sriC,
  tri.cont = SalGla.cont$triC,
  twi.cont = SalGla.cont$twiC,
  shrub_cov.cont = SalGla.cont$SalGla_shrub_coverC,
  gramin_cov.cont = SalGla.cont$graminoid_coverC,
  compet.cont = SalGla.cont$competC,
  N_cont = nrow(SalGla.cont),
  
  # plot group level predictors
  tempjja.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = SalGla.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(SalGla.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(SalGla.tot$competC), to = max(SalGla.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(SalGla.tot$SalGla_shrub_coverC), to = max(SalGla.tot$SalGla_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(SalGla.tot$graminoid_coverC), to = max(SalGla.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(SalGla.tot$sriC), to = max(SalGla.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(SalGla.tot$triC), to = max(SalGla.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(SalGla.tot$twiC), to = max(SalGla.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(SalGla.tot$tempjjaC), to = max(SalGla.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(SalGla.tot$precipjjaC), to = max(SalGla.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(SalGla.tot$tempcontC), to = max(SalGla.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(SalGla.tot$tempjjaC,0.05),quantile(SalGla.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.SalGla.data)

# VacUli ----
shrub_gradient_jags.VacUli.data <- list(
  
  # plot level predictors, for discrete...
  cov.dis = VacUli.dis$cover,
  plotgroup.dis = VacUli.dis$plotgroup.NUM,
  tempjja.dis = VacUli.dis$tempjjaC,
  sri.dis = VacUli.dis$sriC,
  tri.dis = VacUli.dis$triC,
  twi.dis = VacUli.dis$twiC,
  shrub_cov.dis = VacUli.dis$VacUli_shrub_coverC,
  gramin_cov.dis = VacUli.dis$graminoid_coverC,
  compet.dis = VacUli.dis$competC,
  N_discrete = nrow(VacUli.dis),
  
  # ...and continuous part of the data
  cov.cont = VacUli.cont$cover,
  plotgroup.cont = VacUli.cont$plotgroup.NUM,
  tempjja.cont = VacUli.cont$tempjjaC,
  sri.cont = VacUli.cont$sriC,
  tri.cont = VacUli.cont$triC,
  twi.cont = VacUli.cont$twiC,
  shrub_cov.cont = VacUli.cont$VacUli_shrub_coverC,
  gramin_cov.cont = VacUli.cont$graminoid_coverC,
  compet.cont = VacUli.cont$competC,
  N_cont = nrow(VacUli.cont),
  
  # plot group level predictors
  tempjja.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(tempjja.tot = mean(tempjjaC)) %>% pull(tempjja.tot), # one value per tXpg
  tempcont.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(tempcont.tot = mean(tempcontC)) %>% pull(tempcont.tot),
  precipjja.tot = VacUli.tot %>% group_by(plotgroup.NUM) %>% summarise(precipjja.tot = mean(precipjjaC)) %>% pull(precipjja.tot),
  N_plotgroups = length(unique(VacUli.tot$site_alt_plotgroup_id)),
  
  # subset of values for prediction, for each predictor...
  xhat_compet = seq(from = min(VacUli.tot$competC), to = max(VacUli.tot$competC), length.out = 100),
  xhat_shrub_cover = seq(from = min(VacUli.tot$VacUli_shrub_coverC), to = max(VacUli.tot$VacUli_shrub_coverC), length.out = 100),
  xhat_graminoid_cover = seq(from = min(VacUli.tot$graminoid_coverC), to = max(VacUli.tot$graminoid_coverC), length.out = 100),
  xhat_sri = seq(from = min(VacUli.tot$sriC), to = max(VacUli.tot$sriC), length.out = 100),
  xhat_tri = seq(from = min(VacUli.tot$triC), to = max(VacUli.tot$triC), length.out = 100),
  xhat_twi = seq(from = min(VacUli.tot$twiC), to = max(VacUli.tot$twiC), length.out = 100),
  xhat_tempjja = seq(from = min(VacUli.tot$tempjjaC), to = max(VacUli.tot$tempjjaC), length.out = 100),
  xhat_precipjja = seq(from = min(VacUli.tot$precipjjaC), to = max(VacUli.tot$precipjjaC), length.out = 100),
  xhat_tempcont = seq(from = min(VacUli.tot$tempcontC), to = max(VacUli.tot$tempcontC), length.out = 100),
  Nxhat = 100,
  
  # ... and for predicting at high/low temperature levels
  xhat_twi2 = as.numeric(c(quantile(VacUli.tot$tempjjaC,0.05),quantile(VacUli.tot$tempjjaC,0.95))), 
  Nxhat2 = 2
)
str(shrub_gradient_jags.VacUli.data)


# # save model input data
# save(list = c("shrub_gradient_jags.BetNan.data",
#               "shrub_gradient_jags.CasTet.data",
#               "shrub_gradient_jags.EmpNig.data",
#               "shrub_gradient_jags.PhyCae.data",
#               "shrub_gradient_jags.RhoGro.data",
#               "shrub_gradient_jags.RhoTom.data",
#               "shrub_gradient_jags.SalArc.data",
#               "shrub_gradient_jags.SalGla.data",
#               "shrub_gradient_jags.VacUli.data"), 
#      file = file.path("..", "data", "processed", "model_input_data", "shrub_gradient_jags.speciesdata.Rdata"))
```

### > specifying model
```{r}
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] + 
                    b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.spec.jags"))
```

Specify the parameters to be monitored:
```{r}
params <- c("intercept",
            "b.tempjja.x", "b.tempjja.x2",
            "b.tempcont.x", "b.tempcont.x2",
            "b.precipjja.x", "b.precipjja.x2",
            "b.compet",
            "b.shrub_cov", 
            "b.gramin_cov",
            "b.sri",
            "b.tri",
            "b.twi",
            "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
            "sigma.plotgroup",
            "phi",
            "phat_compet", "phat_shrub_cover", "phat_graminoid_cover", 
            "phat_sri", "phat_twi", "phat_tri", 
            "phat_tempjja", "phat_tempcont", "phat_precipjja", 
            "phat_tempXmoist")
```

### > run & evaluate model
<br>

### *Betula nana*
```{r}
# run model
model_out.shrub_gradient.BetNan <- jags(shrub_gradient_jags.BetNan.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text") 

# plot(model_out.shrub_gradient.BetNan) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.BetNan <- model_out.shrub_gradient.BetNan$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.BetNan),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.BetNan <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list)-4)){
  ci_90.BetNan[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.BetNan[param, 3] <- names(data.frame(model_out.shrub_gradient.BetNan$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.BetNan <- coeff.shrub_gradient.BetNan %>% 
  left_join(ci_90.BetNan, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat)

save(coeff.shrub_gradient.BetNan, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_BetNan.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_BetNan.Rdata"))
print(coeff.shrub_gradient.BetNan)
(effect_size_plot.BetNan <- model_plot_marg_function(coeff.shrub_gradient.BetNan, title_string = "Betula nana", plot_width = 12.5))
```

* **negative response to graminoid cover (sig.)** 
* *unimodal response to temp. variability & other shrub cover (m.s.)*

<br>

As the quadratic terms for summer temperature and precipitation were not significant, they were removed from the model before re-running:
```{r}
# new model object with terms removed
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      # b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      # b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        # b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        # b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    # b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      # b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
                                      # b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.BetNan2.jags"))

# specify new set of parameters to be monitored
params_BetNan2 <- c("intercept",
                    "b.tempjja.x", # "b.tempjja.x2",
                    "b.tempcont.x", # "b.tempcont.x2",
                    "b.precipjja.x", # "b.precipjja.x2",
                    "b.compet", 
                    "b.shrub_cov",
                    "b.gramin_cov",
                    "b.sri",
                    "b.tri",
                    "b.twi",
                    "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                    "sigma.plotgroup",
                    "phi",
                    "phat_compet", "phat_shrub_cover", "phat_graminoid_cover", 
                    "phat_sri", "phat_tri", "phat_twi", 
                    "phat_tempjja", "phat_tempcont", "phat_precipjja",
                    "phat_tempXmoist")

model_out.shrub_gradient.BetNan2 <- jags(shrub_gradient_jags.BetNan.data,   # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_BetNan2,                     # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.BetNan2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.BetNan2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.BetNan2 <- model_out.shrub_gradient.BetNan2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")

# add 90% CIs
ci_90.BetNan2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list)-4)){
  ci_90.BetNan2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.BetNan2[param, 3] <- names(data.frame(model_out.shrub_gradient.BetNan2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.BetNan2 <- coeff.shrub_gradient.BetNan2 %>% 
  left_join(ci_90.BetNan2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.BetNan2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_BetNan2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_BetNan2.Rdata"))

# effect size plot
(effect_size_plot.BetNan2 <- model_plot_marg_function(coeff.shrub_gradient.BetNan2, title_string = "Betula nana", plot_width = 9.5))

```

* **positive response to temperature variability, negative response to graminoid cover (sig.)**; *negative response to other shrub cover (m.s.)*

<br>


### *Cassiope tetragona*
```{r}
model_out.shrub_gradient.CasTet <- jags(shrub_gradient_jags.CasTet.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.CasTet) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.CasTet <- model_out.shrub_gradient.CasTet$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.CasTet),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.CasTet <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list)-4)){
  ci_90.CasTet[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.CasTet[param, 3] <- names(data.frame(model_out.shrub_gradient.CasTet$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.CasTet <- coeff.shrub_gradient.CasTet %>% 
  left_join(ci_90.CasTet, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat)

print(coeff.shrub_gradient.CasTet)

save(coeff.shrub_gradient.CasTet, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_CasTet.Rdata"))

```

* **not converging**: really large R-hat values (~ 6.8) (only 16 non-zero values across plot groups)

<br>

### *Empetrum nigrum*
```{r}
model_out.shrub_gradient.EmpNig <- jags(shrub_gradient_jags.EmpNig.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.EmpNig) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.EmpNig <- model_out.shrub_gradient.EmpNig$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.EmpNig),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.EmpNig <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list)-4)){
  ci_90.EmpNig[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.EmpNig[param, 3] <- names(data.frame(model_out.shrub_gradient.EmpNig$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.EmpNig <- coeff.shrub_gradient.EmpNig %>% 
  left_join(ci_90.EmpNig, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.EmpNig, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_EmpNig.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_EmpNig.Rdata"))

# effect size plot
(effect_size_plot.EmpNig <- model_plot_sig_function(coeff.shrub_gradient.EmpNig, title_string = "Empetrum nigrum", plot_width = 12.5))

```

* **positive response to other shrub cover, precipitation (linear), negative response to competition (sig.)**
* slightly negative response to graminoid cover, slightly positive relationship with moisture (n.s.)

<br>

As none of the quadratic terms for climatic variables were significant, they were removed from the model before re-running:
```{r}
# new model object with terms removed
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      # b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      # b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        # b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        # b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    # b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      # b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
                                      # b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.EmpNig2.jags"))

# specify new set of parameters to be monitored
params_EmpNig2 <- c("intercept",
                    "b.tempjja.x", # "b.tempjja.x2",
                    "b.tempcont.x", # "b.tempcont.x2",
                    "b.precipjja.x", # "b.precipjja.x2",
                    "b.compet", 
                    "b.shrub_cov",
                    "b.gramin_cov",
                    "b.sri",
                    "b.tri",
                    "b.twi",
                    "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                    "sigma.plotgroup",
                    "phi",
                    "phat_compet", "phat_shrub_cover", "phat_graminoid_cover", 
                    "phat_sri", "phat_tri", "phat_twi", 
                    "phat_tempjja", "phat_tempcont", "phat_precipjja",
                    "phat_tempXmoist")

model_out.shrub_gradient.EmpNig2 <- jags(shrub_gradient_jags.EmpNig.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_EmpNig2,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.EmpNig2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.EmpNig2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.EmpNig2 <- model_out.shrub_gradient.EmpNig2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")

# add 90% CIs
ci_90.EmpNig2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list)-4)){
  ci_90.EmpNig2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.EmpNig2[param, 3] <- names(data.frame(model_out.shrub_gradient.EmpNig2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.EmpNig2 <- coeff.shrub_gradient.EmpNig2 %>% 
  left_join(ci_90.EmpNig2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.EmpNig2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_EmpNig2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_EmpNig2.Rdata"))

# effect size plot
(effect_size_plot.EmpNig2 <- model_plot_sig_function(coeff.shrub_gradient.EmpNig2, title_string = "Empetrum nigrum", plot_width = 9.5))

```

* **positive response to other shrub cover, precipitation (linear), negative response to competition & temperature variability (sig.)**
* negative response to graminoid cover, positive relationship with moisture (n.s.)

<br>

### *Phyllodoce caerulea*
```{r}
model_out.shrub_gradient.PhyCae <- jags(shrub_gradient_jags.PhyCae.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.PhyCae) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.PhyCae <- model_out.shrub_gradient.PhyCae$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.PhyCae),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.PhyCae <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list)-4)){
  ci_90.PhyCae[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.PhyCae[param, 3] <- names(data.frame(model_out.shrub_gradient.PhyCae$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.PhyCae <- coeff.shrub_gradient.PhyCae %>% 
  left_join(ci_90.PhyCae, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.PhyCae, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_PhyCae.Rdata"))

```

* **not converged** large R-hat values (~ 1.36) (only 10 non-zero values across plot groups)

<br>

### *Rhododendron groenlandicum*
```{r}
model_out.shrub_gradient.RhoGro <- jags(shrub_gradient_jags.RhoGro.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.RhoGro) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.RhoGro <- model_out.shrub_gradient.RhoGro$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoGro),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.RhoGro <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list)-4)){
  ci_90.RhoGro[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.RhoGro[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoGro$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.RhoGro <- coeff.shrub_gradient.RhoGro %>% 
  left_join(ci_90.RhoGro, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.RhoGro, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_RhoGro.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_RhoGro.Rdata"))

# effect size plot
(effect_size_plot.RhoGro <- model_plot_marg_function(coeff.shrub_gradient.RhoGro, title_string = "Rhododendron groenlandicum", plot_width = 12.5))

```

* **positive response to other shrub cover, negative relationship with precipitation, unimodal response to temperature (sig.)** 
* *negative response to graminoid cover (m.s.)*
* slightly positive response to solar radiation, temperature variability (n.s.)
* large variation in competiton response as there was only one (however rare) taller-growing species in the system (*R. tomentosum*)

<br>

As the quadratic terms for temperature variability and precipitation were not significant, they were removed from the model before re-running:
```{r}
# new model object with terms removed
write("
  
  model{
    
    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.RhoGro2.jags"))

# specify new set of parameters to be monitored
params_RhoGro2 <- c("intercept",
                    "b.tempjja.x", "b.tempjja.x2",
                    "b.tempcont.x", # "b.tempcont.x2",
                    "b.precipjja.x", # "b.precipjja.x2",
                    "b.compet", 
                    "b.shrub_cov",
                    "b.gramin_cov",
                    "b.sri",
                    "b.tri",
                    "b.twi",
                    "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                    "sigma.plotgroup",
                    "phi",
                    "phat_compet", 
                    "phat_shrub_cover", "phat_graminoid_cover", 
                    "phat_sri", "phat_tri", "phat_twi", 
                    "phat_tempjja", "phat_tempcont", "phat_precipjja",
                    "phat_tempXmoist")

model_out.shrub_gradient.RhoGro2 <- jags(shrub_gradient_jags.RhoGro.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_RhoGro2,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.RhoGro2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.RhoGro2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.RhoGro2 <- model_out.shrub_gradient.RhoGro2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoGro),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.RhoGro2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list)-4)){
  ci_90.RhoGro2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.RhoGro2[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoGro2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.RhoGro2 <- coeff.shrub_gradient.RhoGro2 %>% 
  left_join(ci_90.RhoGro2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.RhoGro2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoGro2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoGro2.Rdata"))

# effect size plot
(effect_size_plot.RhoGro2 <- model_plot_marg_function(coeff.shrub_gradient.RhoGro2, title_string = "Rhododendron groenlandicum", plot_width = 10.5))

```

* **positive response to temperature variability & other shrub cover, unimodal response to temperature and negative response to precipitation (linear) (sig) **
* *negative relationship with graminoid cover (n.s.)*
* positive response to solar radiation (n.s.)
* large variation in competiton response as there was only one (however rare) taller-growing species in the system (*R. tomentosum*)

<br>

### *Rhododendron tomentosum*
```{r}
model_out.shrub_gradient.RhoTom <- jags(shrub_gradient_jags.RhoTom.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.RhoTom) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.RhoTom <- model_out.shrub_gradient.RhoTom$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.RhoTom),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.RhoTom <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list)-4)){
  ci_90.RhoTom[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.RhoTom[param, 3] <- names(data.frame(model_out.shrub_gradient.RhoTom$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.RhoTom <- coeff.shrub_gradient.RhoTom %>% 
  left_join(ci_90.RhoTom, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.RhoTom, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_RhoTom.Rdata"))

```

* **not converging**: large R-hat values (~ 1.38), large variation in parameter estimates (only 13 non-zero values)

<br>

### *Salix arctophila*
```{r}
model_out.shrub_gradient.SalArc <- jags(shrub_gradient_jags.SalArc.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.SalArc) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.SalArc <- model_out.shrub_gradient.SalArc$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.SalArc),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.SalArc <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list)-4)){
  ci_90.SalArc[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.SalArc[param, 3] <- names(data.frame(model_out.shrub_gradient.SalArc$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.SalArc <- coeff.shrub_gradient.SalArc %>% 
  left_join(ci_90.SalArc, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.SalArc, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalArc.Rdata"))

```

* **not converging well**: relatively large R-hat values (~ 1.37), quite some variation in parameter estimates (only 13 non-zero values across plot groups)

<br>

### *Salix glauca*
```{r}
model_out.shrub_gradient.SalGla <- jags(shrub_gradient_jags.SalGla.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.SalGla) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.SalGla <- model_out.shrub_gradient.SalGla$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.SalGla),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.SalGla <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list)-4)){
  ci_90.SalGla[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.SalGla[param, 3] <- names(data.frame(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.SalGla <- coeff.shrub_gradient.SalGla %>% 
  left_join(ci_90.SalGla, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.SalGla, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_SalGla.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_SalGla.Rdata"))

# effect size plot
(effect_size_plot.SalGla <- model_plot_sig_function(coeff.shrub_gradient.SalGla, title_string = "Salix glauca", plot_width = 12.5))

```

* **positive response to competition (sig.), negative response to other shrub cover (m.s.)**
* slightly negative response to temperature & solar radiation (n.s.)

<br>

As the quadratic terms for summer temperature, temperature variability and summer precipitation were not significant, they were removed from the model before re-running:
```{r}
# new model object with terms removed
write("
  
  model{

    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      # b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      # b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        # b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        # b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    # b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] # + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      # b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] # +
                                      # b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.SalGla2.jags"))

# specify new set of parameters to be monitored
params_SalGla2 <- c("intercept",
                    "b.tempjja.x", # "b.tempjja.x2",
                    "b.tempcont.x", # "b.tempcont.x2",
                    "b.precipjja.x", # "b.precipjja.x2",
                    "b.compet", 
                    "b.shrub_cov",
                    "b.gramin_cov",
                    "b.sri",
                    "b.tri",
                    "b.twi",
                    "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                    "sigma.plotgroup",
                    "phi",
                    "phat_compet", "phat_shrub_cover", "phat_graminoid_cover", 
                    "phat_sri", "phat_tri", "phat_twi", 
                    "phat_tempjja", "phat_tempcont", "phat_precipjja",
                    "phat_tempXmoist")

model_out.shrub_gradient.SalGla2 <- jags(shrub_gradient_jags.SalGla.data,   # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_SalGla2,                     # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.SalGla2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.SalGla2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.SalGla2 <- model_out.shrub_gradient.SalGla2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")

# add 90% CIs
ci_90.SalGla2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.SalGla$BUGSoutput$sims.list)-4)){
  ci_90.SalGla2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.SalGla2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.SalGla2[param, 3] <- names(data.frame(model_out.shrub_gradient.SalGla2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.SalGla2 <- coeff.shrub_gradient.SalGla2 %>% 
  left_join(ci_90.SalGla2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.SalGla2, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalGla2.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "model_output_SalGla2.Rdata"))

# effect size plot
(effect_size_plot.SalGla2 <- model_plot_marg_function(coeff.shrub_gradient.SalGla2, title_string = "Salix glauca", plot_width = 9.5))

```

* **positive response to competition (sig.) and temperature variability (linear, m.s.); negative response to other shrub cover (m.s.)**
* positive response to moisture & terrain heterogeneity, negative response to solar radiation (n.s.)

<br>


### *Vaccinium uliginosum*
```{r}
model_out.shrub_gradient.VacUli <- jags(shrub_gradient_jags.VacUli.data,    # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params,                             # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.spec.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.VacUli) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.VacUli <- model_out.shrub_gradient.VacUli$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")
  # mutate(param = as.vector(sapply(strsplit(rownames(coeff.shrub_gradient.VacUli),"[[]",fixed=FALSE), "[", 1))) #%>% print

# add 90% CIs
ci_90.VacUli <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list)-4)){
  ci_90.VacUli[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.VacUli[param, 3] <- names(data.frame(model_out.shrub_gradient.VacUli$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.VacUli <- coeff.shrub_gradient.VacUli %>% 
  left_join(ci_90.VacUli, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.VacUli, file = file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_VacUli.Rdata"))
# load(file.path("..", "data", "processed", "model_outputs", "species_twi", "first_runs_converged", "model_output_VacUli.Rdata"))

# effect size plot
(effect_size_plot.VacUli <- model_plot_marg_function(coeff.shrub_gradient.VacUli, title_string = "Vaccinium uliginosum", plot_width = 12.5))

```

* **unimodal relationship with summer temperature (sig.)**, *negative relationship with competition (m.s.)*

<br>

As the quadratic terms for summer precipitation and temperature variability were not significant, they were removed from the model before re-running:
```{r}
# new model object with terms removed
write("
  
  model{

    # priors
      
      intercept ~ dnorm(0, 0.0001)
      
      b.compet ~ dnorm(0, 0.0001)
      b.shrub_cov ~ dnorm(0, 0.0001)
      b.gramin_cov ~ dnorm(0, 0.0001)
      b.sri ~ dnorm(0, 0.0001)
      b.tri ~ dnorm(0, 0.0001)
      b.twi ~ dnorm(0, 0.0001)

      sigma.plotgroup ~ dunif(0,100)
      tau.plotgroup <- 1/(sigma.plotgroup * sigma.plotgroup)
      
      b.tempjja.x ~ dnorm(0, 0.001)
      b.tempjja.x2 ~ dnorm(0, 0.001)
      b.tempcont.x ~ dnorm(0, 0.001)
      # b.tempcont.x2 ~ dnorm(0, 0.001)
      b.precipjja.x ~ dnorm(0, 0.001)
      # b.precipjja.x2 ~ dnorm(0, 0.001)
      
      b.tempXtwi ~ dnorm(0, 0.001)
      b.tempXtwi2 ~ dnorm(0, 0.001)
      
      phi ~ dgamma(0.1, 0.1)
      
      
    # LIKELIHOOD for discrete part

      for (i in 1:N_discrete){ 
        cov.dis[i] ~ dbern(mu[i])
        logit(mu[i]) <- b_plotgroup[plotgroup.dis[i]] + # ~= random effect of plot group
                        b.compet * compet.dis[i] + 
                        b.shrub_cov * shrub_cov.dis[i] + 
                        b.gramin_cov * gramin_cov.dis[i] + 
                        b.tempXtwi * tempjja.dis[i] * twi.dis[i] +       # for interaction
                        b.tempXtwi2 * (tempjja.dis[i]^2) * twi.dis[i] +  # for interaction
                        b.twi * twi.dis[i] + 
                        b.sri * sri.dis[i] +
                        b.tri * tri.dis[i]
      }
      
      
    # LIKELIHOOD for continuous part

      for (j in 1:N_cont){
        cov.cont[j] ~ dbeta(p[j], q[j])
        p[j] <- mu2[j] * phi
        q[j] <- (1 - mu2[j]) * phi
        logit(mu2[j]) <- b_plotgroup[plotgroup.cont[j]] + # ~= random effect of plot group
                        b.compet * compet.cont[j] +
                        b.shrub_cov * shrub_cov.cont[j] +
                        b.gramin_cov * gramin_cov.cont[j] +
                        b.tempXtwi * tempjja.cont[j] * twi.cont[j] +       # for interaction
                        b.tempXtwi2 * (tempjja.cont[j]^2) * twi.cont[j] +  # for interaction
                        b.twi * twi.cont[j] + 
                        b.sri * sri.cont[j] +
                        b.tri * tri.cont[j]
      }


      for (k in 1:N_plotgroups){ # length of total plotgroups
        b_plotgroup[k] ~ dnorm(mu.plotgroup[k],tau.plotgroup)
        mu.plotgroup[k] <- intercept + 
                    
                    # plot group level predictors, linear and quadratic term
                    b.tempjja.x * tempjja.tot[k] + 
                    b.tempjja.x2 * (tempjja.tot[k]^2) + 
                    b.tempcont.x * tempcont.tot[k] + 
                    # b.tempcont.x2 * (tempcont.tot[k]^2) +
                    b.precipjja.x * precipjja.tot[k] # + 
                    # b.precipjja.x2 * (precipjja.tot[k]^2) 
      }
      
      
      # add predicted values (derived parameters)
      for (m in 1:Nxhat){
        phat_compet[m] <- intercept + b.compet * xhat_compet[m]
        phat_graminoid_cover[m] <- intercept + b.gramin_cov * xhat_graminoid_cover[m]
        phat_shrub_cover[m] <- intercept + b.shrub_cov * xhat_shrub_cover[m]
        phat_sri[m] <- intercept + b.sri * xhat_sri[m]
        phat_tri[m] <- intercept + b.tri * xhat_tri[m]
        phat_twi[m] <- intercept + b.twi * xhat_twi[m]
        phat_tempjja[m] <- intercept + b.tempjja.x * xhat_tempjja[m] + b.tempjja.x2 * (xhat_tempjja[m]^2)
        phat_tempcont[m] <- intercept + b.tempcont.x * xhat_tempcont[m] # + b.tempcont.x2 * (xhat_tempcont[m]^2)
        phat_precipjja[m] <- intercept + b.precipjja.x * xhat_precipjja[m] # + b.precipjja.x2 * (xhat_precipjja[m]^2)
      
        for (p in 1:Nxhat2){
          phat_tempXmoist[m,p] <- intercept +
                                      b.tempjja.x * xhat_tempjja[m] +
                                      b.tempjja.x2 * (xhat_tempjja[m]^2) +
                                      b.twi * xhat_twi2[p] +
                                      b.tempXtwi * xhat_tempjja[m] * xhat_twi2[p] +
                                      b.tempXtwi2 * (xhat_tempjja[m]^2) * xhat_twi2[p]
          }
        }

    
      }
  ", file.path("..", "models_twi", "shrub_gradient.VacUli2.jags"))

# specify new set of parameters to be monitored
params_VacUli2 <- c("intercept",
                    "b.tempjja.x", # "b.tempjja.x2",
                    "b.tempcont.x", # "b.tempcont.x2",
                    "b.precipjja.x", # "b.precipjja.x2",
                    "b.compet",
                    "b.shrub_cov",
                    "b.gramin_cov",
                    "b.sri",
                    "b.tri",
                    "b.twi",
                    "b_plotgroup[1]","b_plotgroup[2]","b_plotgroup[3]","b_plotgroup[63]",
                    "sigma.plotgroup",
                    "phi",
                    "phat_compet", "phat_shrub_cover", "phat_graminoid_cover", 
                    "phat_sri", "phat_tri", "phat_twi", 
                    "phat_tempjja", "phat_tempcont", "phat_precipjja",
                    "phat_tempXmoist")

model_out.shrub_gradient.VacUli2 <- jags(shrub_gradient_jags.VacUli.data,   # input data
                                        inits = NULL,                       # JAGS to create initial values
                                        params_VacUli2,                     # parameters to be saved
                                        model.file = file.path("..", "models_twi", "shrub_gradient.VacUli2.jags"), 
                                        n.chains = 3,                       # no. Markov chains
                                        n.iter = 100000, n.burnin = 70000,  # no. iterations & burn-in fraction per chain
                                        n.thin = 2,                         # thinning rate
                                        DIC = FALSE,                        # do not compute deviance, pD, and DIC
                                        working.directory = NULL, 
                                        progress.bar = "text")

# plot(model_out.shrub_gradient.VacUli2) #check convergence, etc.
```

Extract coefficients and plot effect sizes:
```{r, warning = FALSE}
# extract coefficients 
coeff.shrub_gradient.VacUli2 <- model_out.shrub_gradient.VacUli2$BUGSoutput$summary %>% 
  as.data.frame %>% 
  select('mean','sd','2.5%','97.5%','Rhat') %>% 
# add identifying info to data frame
  rownames_to_column(var = "param")

# add 90% CIs
ci_90.VacUli2 <- data.frame(q5 = NA, q95 = NA, param = NA)
for (param in 1:(length(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list)-4)){
  ci_90.VacUli2[param,1:2] <- quantile(data.frame(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list[param])[,1], probs = c(0.05, 0.95))
  ci_90.VacUli2[param, 3] <- names(data.frame(model_out.shrub_gradient.VacUli2$BUGSoutput$sims.list))[param]
}

# join to coefficients table
coeff.shrub_gradient.VacUli2 <- coeff.shrub_gradient.VacUli2 %>% 
  left_join(ci_90.VacUli2, by = "param") %>% 
  # reorder and rename cols
  select(param, mean, sd, 
         l95 = "2.5%",
         l90 = q5,
         u90 = q95,
         u95 = "97.5%",
         Rhat) %>% print

save(coeff.shrub_gradient.VacUli2, file = file.path("..", "data", "processed", "model_outputs", "model_output_VacUli2.Rdata"))

# effect size plot
(effect_size_plot.VacUli2 <- model_plot_marg_function(coeff.shrub_gradient.VacUli2, title_string = "Vaccinium uliginosum", plot_width = 9.5))

```

* *negative relationship with competition (m.s.)*
* negative relationship with moisture, positive response to solar radiation (n.s.)

<br>

